Overview

Dataset statistics

Number of variables153
Number of observations953
Missing cells119218
Missing cells (%)81.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory1.2 KiB

Variable types

Categorical114
Boolean11
Numeric18
Unsupported9
Text1

Alerts

DATAFLOW has constant value ""Constant
LAST UPDATE has constant value ""Constant
freq has constant value ""Constant
ESTAT:ILC_LI02(1.0)_unit has constant value ""Constant
indic_il has constant value ""Constant
sex has constant value ""Constant
age has constant value ""Constant
OBS_FLAG has constant value ""Constant
DATAFLOW_1 has constant value ""Constant
LAST UPDATE_1 has constant value ""Constant
freq_1 has constant value ""Constant
ESTAT:ILC_PW01(1.0)_unit has constant value ""Constant
isced11 has constant value ""Constant
indic_wb has constant value ""Constant
sex_1 has constant value ""Constant
age_1 has constant value ""Constant
DATAFLOW_2 has constant value ""Constant
LAST UPDATE_2 has constant value ""Constant
freq_2 has constant value ""Constant
ESTAT:ILC_PW01(1.0)_unit_2 has constant value ""Constant
isced11_2 has constant value ""Constant
indic_wb_2 has constant value ""Constant
sex_2 has constant value ""Constant
age_2 has constant value ""Constant
DATAFLOW_3 has constant value ""Constant
LAST UPDATE_3 has constant value ""Constant
freq_3 has constant value ""Constant
ESTAT:ILC_PW01(1.0)_unit_3 has constant value ""Constant
isced11_3 has constant value ""Constant
indic_wb_3 has constant value ""Constant
sex_3 has constant value ""Constant
age_3 has constant value ""Constant
DATAFLOW_4 has constant value ""Constant
LAST UPDATE_4 has constant value ""Constant
freq_4 has constant value ""Constant
ESTAT:ILC_PW01(1.0)_unit_4 has constant value ""Constant
isced11_4 has constant value ""Constant
indic_wb_4 has constant value ""Constant
sex_4 has constant value ""Constant
age_4 has constant value ""Constant
DATAFLOW_5 has constant value ""Constant
LAST UPDATE_5 has constant value ""Constant
freq_5 has constant value ""Constant
ESTAT:ILC_PW01(1.0)_unit_5 has constant value ""Constant
isced11_5 has constant value ""Constant
indic_wb_5 has constant value ""Constant
sex_5 has constant value ""Constant
age_5 has constant value ""Constant
DATAFLOW_6 has constant value ""Constant
LAST UPDATE_6 has constant value ""Constant
freq_6 has constant value ""Constant
ESTAT:ILC_PW01(1.0)_unit_6 has constant value ""Constant
isced11_6 has constant value ""Constant
indic_wb_6 has constant value ""Constant
sex_6 has constant value ""Constant
age_6 has constant value ""Constant
OBS_FLAG_6 has constant value ""Constant
DATAFLOW_7 has constant value ""Constant
LAST UPDATE_7 has constant value ""Constant
freq_7 has constant value ""Constant
ESTAT:ILC_PW01(1.0)_unit_7 has constant value ""Constant
isced11_7 has constant value ""Constant
indic_wb_7 has constant value ""Constant
sex_7 has constant value ""Constant
age_7 has constant value ""Constant
DATAFLOW_8 has constant value ""Constant
LAST UPDATE_8 has constant value ""Constant
freq_8 has constant value ""Constant
ESTAT:ILC_PW01(1.0)_unit_8 has constant value ""Constant
isced11_8 has constant value ""Constant
indic_wb_8 has constant value ""Constant
sex_8 has constant value ""Constant
age_8 has constant value ""Constant
DATAFLOW_9 has constant value ""Constant
LAST UPDATE_9 has constant value ""Constant
freq_9 has constant value ""Constant
ESTAT:ILC_PW01(1.0)_unit_9 has constant value ""Constant
isced11_9 has constant value ""Constant
indic_wb_9 has constant value ""Constant
sex_9 has constant value ""Constant
age_9 has constant value ""Constant
DATAFLOW_10 has constant value ""Constant
LAST UPDATE_10 has constant value ""Constant
freq_10 has constant value ""Constant
ESTAT:ILC_PW01(1.0)_unit_10 has constant value ""Constant
isced11_10 has constant value ""Constant
indic_wb_10 has constant value ""Constant
sex_10 has constant value ""Constant
age_10 has constant value ""Constant
DATAFLOW_11 has constant value ""Constant
LAST UPDATE_11 has constant value ""Constant
freq_11 has constant value ""Constant
c_resid has constant value ""Constant
ESTAT:TOUR_OCC_NINATS(1.0)_unit has constant value ""Constant
hotelsize has constant value ""Constant
DATAFLOW_12 has constant value ""Constant
LAST UPDATE_12 has constant value ""Constant
freq_12 has constant value ""Constant
ESTAT:RAIL_TF_PASSMOV(1.0)_unit has constant value ""Constant
vehicle has constant value ""Constant
DATAFLOW_13 has constant value ""Constant
LAST UPDATE_13 has constant value ""Constant
freq_13 has constant value ""Constant
ESTAT:RAIL_PA_TOTAL(1.0)_unit has constant value ""Constant
DATAFLOW_14 has constant value ""Constant
LAST UPDATE_14 has constant value ""Constant
freq_14 has constant value ""Constant
ESTAT:RAIL_PA_TOTAL(1.0)_unit_14 has constant value ""Constant
DATAFLOW_15 has constant value ""Constant
LAST UPDATE_15 has constant value ""Constant
freq_15 has constant value ""Constant
ESTAT:RAIL_AC_CATNMBR(1.0)_unit has constant value ""Constant
accident has constant value ""Constant
OBS_FLAG_15 has constant value ""Constant
DATAFLOW_16 has constant value ""Constant
LAST UPDATE_16 has constant value ""Constant
freq_16 has constant value ""Constant
tra_infr has constant value ""Constant
n_tracks has constant value ""Constant
ESTAT:TTR00003(1.0)_unit has constant value ""Constant
TIME_PERIOD is highly overall correlated with OBS_FLAG_12High correlation
ESTAT:ILC_LI02(1.0)_VALUE is highly overall correlated with ESTAT:ILC_PW01(1.0)_VALUE and 12 other fieldsHigh correlation
ESTAT:ILC_PW01(1.0)_VALUE is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 13 other fieldsHigh correlation
ESTAT:ILC_PW01(1.0)_VALUE_2 is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 13 other fieldsHigh correlation
ESTAT:ILC_PW01(1.0)_VALUE_3 is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 14 other fieldsHigh correlation
ESTAT:ILC_PW01(1.0)_VALUE_4 is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 13 other fieldsHigh correlation
ESTAT:ILC_PW01(1.0)_VALUE_5 is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 13 other fieldsHigh correlation
ESTAT:ILC_PW01(1.0)_VALUE_6 is highly overall correlated with ESTAT:ILC_PW01(1.0)_VALUE and 11 other fieldsHigh correlation
ESTAT:ILC_PW01(1.0)_VALUE_7 is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 13 other fieldsHigh correlation
ESTAT:ILC_PW01(1.0)_VALUE_8 is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 13 other fieldsHigh correlation
ESTAT:ILC_PW01(1.0)_VALUE_9 is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 12 other fieldsHigh correlation
ESTAT:ILC_PW01(1.0)_VALUE_10 is highly overall correlated with ESTAT:ILC_PW01(1.0)_VALUE and 12 other fieldsHigh correlation
ESTAT:TOUR_OCC_NINATS(1.0)_VALUE is highly overall correlated with ESTAT:RAIL_TF_PASSMOV(1.0)_VALUE and 9 other fieldsHigh correlation
ESTAT:RAIL_TF_PASSMOV(1.0)_VALUE is highly overall correlated with ESTAT:ILC_PW01(1.0)_VALUE_3 and 7 other fieldsHigh correlation
ESTAT:RAIL_PA_TOTAL(1.0)_VALUE is highly overall correlated with ESTAT:TOUR_OCC_NINATS(1.0)_VALUE and 7 other fieldsHigh correlation
ESTAT:RAIL_PA_TOTAL(1.0)_VALUE_14 is highly overall correlated with ESTAT:TOUR_OCC_NINATS(1.0)_VALUE and 9 other fieldsHigh correlation
ESTAT:RAIL_AC_CATNMBR(1.0)_VALUE is highly overall correlated with ESTAT:TOUR_OCC_NINATS(1.0)_VALUE and 9 other fieldsHigh correlation
ESTAT:TTR00003(1.0)_VALUE is highly overall correlated with ESTAT:TOUR_OCC_NINATS(1.0)_VALUE and 9 other fieldsHigh correlation
geo is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 15 other fieldsHigh correlation
OBS_FLAG_11 is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 8 other fieldsHigh correlation
OBS_FLAG_12 is highly overall correlated with TIME_PERIOD and 7 other fieldsHigh correlation
OBS_FLAG_13 is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 16 other fieldsHigh correlation
OBS_FLAG_14 is highly overall correlated with ESTAT:ILC_LI02(1.0)_VALUE and 16 other fieldsHigh correlation
OBS_FLAG_16 is highly overall correlated with ESTAT:ILC_PW01(1.0)_VALUE and 15 other fieldsHigh correlation
OBS_FLAG_13 is highly imbalanced (72.9%)Imbalance
OBS_FLAG_14 is highly imbalanced (72.9%)Imbalance
DATAFLOW has 338 (35.5%) missing valuesMissing
LAST UPDATE has 338 (35.5%) missing valuesMissing
freq has 338 (35.5%) missing valuesMissing
ESTAT:ILC_LI02(1.0)_unit has 338 (35.5%) missing valuesMissing
indic_il has 338 (35.5%) missing valuesMissing
sex has 338 (35.5%) missing valuesMissing
age has 338 (35.5%) missing valuesMissing
ESTAT:ILC_LI02(1.0)_VALUE has 338 (35.5%) missing valuesMissing
OBS_FLAG has 912 (95.7%) missing valuesMissing
DATAFLOW_1 has 926 (97.2%) missing valuesMissing
LAST UPDATE_1 has 926 (97.2%) missing valuesMissing
freq_1 has 926 (97.2%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_unit has 926 (97.2%) missing valuesMissing
isced11 has 926 (97.2%) missing valuesMissing
indic_wb has 926 (97.2%) missing valuesMissing
sex_1 has 926 (97.2%) missing valuesMissing
age_1 has 926 (97.2%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_VALUE has 926 (97.2%) missing valuesMissing
OBS_FLAG_1 has 953 (100.0%) missing valuesMissing
DATAFLOW_2 has 926 (97.2%) missing valuesMissing
LAST UPDATE_2 has 926 (97.2%) missing valuesMissing
freq_2 has 926 (97.2%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_unit_2 has 926 (97.2%) missing valuesMissing
isced11_2 has 926 (97.2%) missing valuesMissing
indic_wb_2 has 926 (97.2%) missing valuesMissing
sex_2 has 926 (97.2%) missing valuesMissing
age_2 has 926 (97.2%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_VALUE_2 has 926 (97.2%) missing valuesMissing
OBS_FLAG_2 has 953 (100.0%) missing valuesMissing
DATAFLOW_3 has 899 (94.3%) missing valuesMissing
LAST UPDATE_3 has 899 (94.3%) missing valuesMissing
freq_3 has 899 (94.3%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_unit_3 has 899 (94.3%) missing valuesMissing
isced11_3 has 899 (94.3%) missing valuesMissing
indic_wb_3 has 899 (94.3%) missing valuesMissing
sex_3 has 899 (94.3%) missing valuesMissing
age_3 has 899 (94.3%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_VALUE_3 has 899 (94.3%) missing valuesMissing
OBS_FLAG_3 has 953 (100.0%) missing valuesMissing
DATAFLOW_4 has 926 (97.2%) missing valuesMissing
LAST UPDATE_4 has 926 (97.2%) missing valuesMissing
freq_4 has 926 (97.2%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_unit_4 has 926 (97.2%) missing valuesMissing
isced11_4 has 926 (97.2%) missing valuesMissing
indic_wb_4 has 926 (97.2%) missing valuesMissing
sex_4 has 926 (97.2%) missing valuesMissing
age_4 has 926 (97.2%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_VALUE_4 has 926 (97.2%) missing valuesMissing
OBS_FLAG_4 has 953 (100.0%) missing valuesMissing
DATAFLOW_5 has 899 (94.3%) missing valuesMissing
LAST UPDATE_5 has 899 (94.3%) missing valuesMissing
freq_5 has 899 (94.3%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_unit_5 has 899 (94.3%) missing valuesMissing
isced11_5 has 899 (94.3%) missing valuesMissing
indic_wb_5 has 899 (94.3%) missing valuesMissing
sex_5 has 899 (94.3%) missing valuesMissing
age_5 has 899 (94.3%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_VALUE_5 has 899 (94.3%) missing valuesMissing
OBS_FLAG_5 has 953 (100.0%) missing valuesMissing
DATAFLOW_6 has 845 (88.7%) missing valuesMissing
LAST UPDATE_6 has 845 (88.7%) missing valuesMissing
freq_6 has 845 (88.7%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_unit_6 has 845 (88.7%) missing valuesMissing
isced11_6 has 845 (88.7%) missing valuesMissing
indic_wb_6 has 845 (88.7%) missing valuesMissing
sex_6 has 845 (88.7%) missing valuesMissing
age_6 has 845 (88.7%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_VALUE_6 has 845 (88.7%) missing valuesMissing
OBS_FLAG_6 has 951 (99.8%) missing valuesMissing
DATAFLOW_7 has 926 (97.2%) missing valuesMissing
LAST UPDATE_7 has 926 (97.2%) missing valuesMissing
freq_7 has 926 (97.2%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_unit_7 has 926 (97.2%) missing valuesMissing
isced11_7 has 926 (97.2%) missing valuesMissing
indic_wb_7 has 926 (97.2%) missing valuesMissing
sex_7 has 926 (97.2%) missing valuesMissing
age_7 has 926 (97.2%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_VALUE_7 has 926 (97.2%) missing valuesMissing
OBS_FLAG_7 has 953 (100.0%) missing valuesMissing
DATAFLOW_8 has 926 (97.2%) missing valuesMissing
LAST UPDATE_8 has 926 (97.2%) missing valuesMissing
freq_8 has 926 (97.2%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_unit_8 has 926 (97.2%) missing valuesMissing
isced11_8 has 926 (97.2%) missing valuesMissing
indic_wb_8 has 926 (97.2%) missing valuesMissing
sex_8 has 926 (97.2%) missing valuesMissing
age_8 has 926 (97.2%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_VALUE_8 has 926 (97.2%) missing valuesMissing
OBS_FLAG_8 has 953 (100.0%) missing valuesMissing
DATAFLOW_9 has 899 (94.3%) missing valuesMissing
LAST UPDATE_9 has 899 (94.3%) missing valuesMissing
freq_9 has 899 (94.3%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_unit_9 has 899 (94.3%) missing valuesMissing
isced11_9 has 899 (94.3%) missing valuesMissing
indic_wb_9 has 899 (94.3%) missing valuesMissing
sex_9 has 899 (94.3%) missing valuesMissing
age_9 has 899 (94.3%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_VALUE_9 has 899 (94.3%) missing valuesMissing
OBS_FLAG_9 has 953 (100.0%) missing valuesMissing
DATAFLOW_10 has 899 (94.3%) missing valuesMissing
LAST UPDATE_10 has 899 (94.3%) missing valuesMissing
freq_10 has 899 (94.3%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_unit_10 has 899 (94.3%) missing valuesMissing
isced11_10 has 899 (94.3%) missing valuesMissing
indic_wb_10 has 899 (94.3%) missing valuesMissing
sex_10 has 899 (94.3%) missing valuesMissing
age_10 has 899 (94.3%) missing valuesMissing
ESTAT:ILC_PW01(1.0)_VALUE_10 has 899 (94.3%) missing valuesMissing
OBS_FLAG_10 has 953 (100.0%) missing valuesMissing
DATAFLOW_11 has 658 (69.0%) missing valuesMissing
LAST UPDATE_11 has 658 (69.0%) missing valuesMissing
freq_11 has 658 (69.0%) missing valuesMissing
c_resid has 658 (69.0%) missing valuesMissing
ESTAT:TOUR_OCC_NINATS(1.0)_unit has 658 (69.0%) missing valuesMissing
hotelsize has 658 (69.0%) missing valuesMissing
ESTAT:TOUR_OCC_NINATS(1.0)_VALUE has 658 (69.0%) missing valuesMissing
OBS_FLAG_11 has 938 (98.4%) missing valuesMissing
DATAFLOW_12 has 170 (17.8%) missing valuesMissing
LAST UPDATE_12 has 170 (17.8%) missing valuesMissing
freq_12 has 170 (17.8%) missing valuesMissing
ESTAT:RAIL_TF_PASSMOV(1.0)_unit has 170 (17.8%) missing valuesMissing
vehicle has 170 (17.8%) missing valuesMissing
ESTAT:RAIL_TF_PASSMOV(1.0)_VALUE has 170 (17.8%) missing valuesMissing
OBS_FLAG_12 has 938 (98.4%) missing valuesMissing
DATAFLOW_13 has 481 (50.5%) missing valuesMissing
LAST UPDATE_13 has 481 (50.5%) missing valuesMissing
freq_13 has 481 (50.5%) missing valuesMissing
ESTAT:RAIL_PA_TOTAL(1.0)_unit has 481 (50.5%) missing valuesMissing
ESTAT:RAIL_PA_TOTAL(1.0)_VALUE has 523 (54.9%) missing valuesMissing
OBS_FLAG_13 has 910 (95.5%) missing valuesMissing
DATAFLOW_14 has 481 (50.5%) missing valuesMissing
LAST UPDATE_14 has 481 (50.5%) missing valuesMissing
freq_14 has 481 (50.5%) missing valuesMissing
ESTAT:RAIL_PA_TOTAL(1.0)_unit_14 has 481 (50.5%) missing valuesMissing
ESTAT:RAIL_PA_TOTAL(1.0)_VALUE_14 has 522 (54.8%) missing valuesMissing
OBS_FLAG_14 has 910 (95.5%) missing valuesMissing
DATAFLOW_15 has 627 (65.8%) missing valuesMissing
LAST UPDATE_15 has 627 (65.8%) missing valuesMissing
freq_15 has 627 (65.8%) missing valuesMissing
ESTAT:RAIL_AC_CATNMBR(1.0)_unit has 627 (65.8%) missing valuesMissing
accident has 627 (65.8%) missing valuesMissing
ESTAT:RAIL_AC_CATNMBR(1.0)_VALUE has 653 (68.5%) missing valuesMissing
OBS_FLAG_15 has 927 (97.3%) missing valuesMissing
DATAFLOW_16 has 653 (68.5%) missing valuesMissing
LAST UPDATE_16 has 653 (68.5%) missing valuesMissing
freq_16 has 653 (68.5%) missing valuesMissing
tra_infr has 653 (68.5%) missing valuesMissing
n_tracks has 653 (68.5%) missing valuesMissing
ESTAT:TTR00003(1.0)_unit has 653 (68.5%) missing valuesMissing
ESTAT:TTR00003(1.0)_VALUE has 653 (68.5%) missing valuesMissing
OBS_FLAG_16 has 934 (98.0%) missing valuesMissing
OBS_FLAG_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
OBS_FLAG_2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
OBS_FLAG_3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
OBS_FLAG_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
OBS_FLAG_5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
OBS_FLAG_7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
OBS_FLAG_8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
OBS_FLAG_9 is an unsupported type, check if it needs cleaning or further analysisUnsupported
OBS_FLAG_10 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-01-10 02:02:48.721714
Analysis finished2024-01-10 02:03:40.479490
Duration51.76 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

DATAFLOW
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing338
Missing (%)35.5%
Memory size7.6 KiB
ESTAT:ILC_LI02(1.0)
615 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters11685
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_LI02(1.0)
2nd rowESTAT:ILC_LI02(1.0)
3rd rowESTAT:ILC_LI02(1.0)
4th rowESTAT:ILC_LI02(1.0)
5th rowESTAT:ILC_LI02(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_LI02(1.0) 615
64.5%
(Missing) 338
35.5%

Length

2024-01-10T02:03:40.560327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:40.677096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_li02(1.0 615
100.0%

Most occurring characters

ValueCountFrequency (%)
T 1230
 
10.5%
I 1230
 
10.5%
L 1230
 
10.5%
0 1230
 
10.5%
E 615
 
5.3%
S 615
 
5.3%
A 615
 
5.3%
: 615
 
5.3%
C 615
 
5.3%
_ 615
 
5.3%
Other values (5) 3075
26.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6150
52.6%
Decimal Number 2460
 
21.1%
Other Punctuation 1230
 
10.5%
Connector Punctuation 615
 
5.3%
Open Punctuation 615
 
5.3%
Close Punctuation 615
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1230
20.0%
I 1230
20.0%
L 1230
20.0%
E 615
10.0%
S 615
10.0%
A 615
10.0%
C 615
10.0%
Decimal Number
ValueCountFrequency (%)
0 1230
50.0%
2 615
25.0%
1 615
25.0%
Other Punctuation
ValueCountFrequency (%)
: 615
50.0%
. 615
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 615
100.0%
Open Punctuation
ValueCountFrequency (%)
( 615
100.0%
Close Punctuation
ValueCountFrequency (%)
) 615
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6150
52.6%
Common 5535
47.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1230
22.2%
: 615
11.1%
_ 615
11.1%
2 615
11.1%
( 615
11.1%
1 615
11.1%
. 615
11.1%
) 615
11.1%
Latin
ValueCountFrequency (%)
T 1230
20.0%
I 1230
20.0%
L 1230
20.0%
E 615
10.0%
S 615
10.0%
A 615
10.0%
C 615
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1230
 
10.5%
I 1230
 
10.5%
L 1230
 
10.5%
0 1230
 
10.5%
E 615
 
5.3%
S 615
 
5.3%
A 615
 
5.3%
: 615
 
5.3%
C 615
 
5.3%
_ 615
 
5.3%
Other values (5) 3075
26.3%

LAST UPDATE
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing338
Missing (%)35.5%
Memory size7.6 KiB
15/12/23 23:00:00
615 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters10455
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 615
64.5%
(Missing) 338
35.5%

Length

2024-01-10T02:03:40.771127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:40.881403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 615
50.0%
23:00:00 615
50.0%

Most occurring characters

ValueCountFrequency (%)
0 2460
23.5%
2 1845
17.6%
1 1230
11.8%
/ 1230
11.8%
3 1230
11.8%
: 1230
11.8%
5 615
 
5.9%
615
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7380
70.6%
Other Punctuation 2460
 
23.5%
Space Separator 615
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2460
33.3%
2 1845
25.0%
1 1230
16.7%
3 1230
16.7%
5 615
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 1230
50.0%
: 1230
50.0%
Space Separator
ValueCountFrequency (%)
615
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10455
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2460
23.5%
2 1845
17.6%
1 1230
11.8%
/ 1230
11.8%
3 1230
11.8%
: 1230
11.8%
5 615
 
5.9%
615
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2460
23.5%
2 1845
17.6%
1 1230
11.8%
/ 1230
11.8%
3 1230
11.8%
: 1230
11.8%
5 615
 
5.9%
615
 
5.9%

freq
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing338
Missing (%)35.5%
Memory size7.6 KiB
A
615 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters615
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 615
64.5%
(Missing) 338
35.5%

Length

2024-01-10T02:03:40.975425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:41.085242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 615
100.0%

Most occurring characters

ValueCountFrequency (%)
A 615
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 615
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 615
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 615
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 615
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 615
100.0%

ESTAT:ILC_LI02(1.0)_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing338
Missing (%)35.5%
Memory size7.6 KiB
PC
615 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1230
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPC
2nd rowPC
3rd rowPC
4th rowPC
5th rowPC

Common Values

ValueCountFrequency (%)
PC 615
64.5%
(Missing) 338
35.5%

Length

2024-01-10T02:03:41.179463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:41.289736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
pc 615
100.0%

Most occurring characters

ValueCountFrequency (%)
P 615
50.0%
C 615
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1230
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 615
50.0%
C 615
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1230
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 615
50.0%
C 615
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 615
50.0%
C 615
50.0%

indic_il
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing338
Missing (%)35.5%
Memory size7.6 KiB
LI_R_MD60
615 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters5535
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLI_R_MD60
2nd rowLI_R_MD60
3rd rowLI_R_MD60
4th rowLI_R_MD60
5th rowLI_R_MD60

Common Values

ValueCountFrequency (%)
LI_R_MD60 615
64.5%
(Missing) 338
35.5%

Length

2024-01-10T02:03:41.368492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:41.478067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
li_r_md60 615
100.0%

Most occurring characters

ValueCountFrequency (%)
_ 1230
22.2%
L 615
11.1%
I 615
11.1%
R 615
11.1%
M 615
11.1%
D 615
11.1%
6 615
11.1%
0 615
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3075
55.6%
Connector Punctuation 1230
 
22.2%
Decimal Number 1230
 
22.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 615
20.0%
I 615
20.0%
R 615
20.0%
M 615
20.0%
D 615
20.0%
Decimal Number
ValueCountFrequency (%)
6 615
50.0%
0 615
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3075
55.6%
Common 2460
44.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 615
20.0%
I 615
20.0%
R 615
20.0%
M 615
20.0%
D 615
20.0%
Common
ValueCountFrequency (%)
_ 1230
50.0%
6 615
25.0%
0 615
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1230
22.2%
L 615
11.1%
I 615
11.1%
R 615
11.1%
M 615
11.1%
D 615
11.1%
6 615
11.1%
0 615
11.1%

sex
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing338
Missing (%)35.5%
Memory size2.0 KiB
True
615 
(Missing)
338 
ValueCountFrequency (%)
True 615
64.5%
(Missing) 338
35.5%
2024-01-10T02:03:41.572250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing338
Missing (%)35.5%
Memory size7.6 KiB
TOTAL
615 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3075
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 615
64.5%
(Missing) 338
35.5%

Length

2024-01-10T02:03:41.666858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:41.776376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 615
100.0%

Most occurring characters

ValueCountFrequency (%)
T 1230
40.0%
O 615
20.0%
A 615
20.0%
L 615
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3075
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1230
40.0%
O 615
20.0%
A 615
20.0%
L 615
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3075
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1230
40.0%
O 615
20.0%
A 615
20.0%
L 615
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1230
40.0%
O 615
20.0%
A 615
20.0%
L 615
20.0%

geo
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
SE
 
53
BE
 
44
FR
 
44
DK
 
44
ES
 
43
Other values (22)
725 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1906
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAT
2nd rowAT
3rd rowAT
4th rowAT
5th rowAT

Common Values

ValueCountFrequency (%)
SE 53
 
5.6%
BE 44
 
4.6%
FR 44
 
4.6%
DK 44
 
4.6%
ES 43
 
4.5%
PT 43
 
4.5%
DE 42
 
4.4%
EL 41
 
4.3%
LU 41
 
4.3%
NL 39
 
4.1%
Other values (17) 519
54.5%

Length

2024-01-10T02:03:41.870981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se 53
 
5.6%
be 44
 
4.6%
fr 44
 
4.6%
dk 44
 
4.6%
es 43
 
4.5%
pt 43
 
4.5%
de 42
 
4.4%
el 41
 
4.3%
lu 41
 
4.3%
nl 39
 
4.1%
Other values (17) 519
54.5%

Most occurring characters

ValueCountFrequency (%)
E 316
16.6%
L 217
11.4%
T 163
 
8.6%
S 160
 
8.4%
I 135
 
7.1%
R 104
 
5.5%
D 86
 
4.5%
F 78
 
4.1%
B 77
 
4.0%
K 75
 
3.9%
Other values (12) 495
26.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1906
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 316
16.6%
L 217
11.4%
T 163
 
8.6%
S 160
 
8.4%
I 135
 
7.1%
R 104
 
5.5%
D 86
 
4.5%
F 78
 
4.1%
B 77
 
4.0%
K 75
 
3.9%
Other values (12) 495
26.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1906
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 316
16.6%
L 217
11.4%
T 163
 
8.6%
S 160
 
8.4%
I 135
 
7.1%
R 104
 
5.5%
D 86
 
4.5%
F 78
 
4.1%
B 77
 
4.0%
K 75
 
3.9%
Other values (12) 495
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 316
16.6%
L 217
11.4%
T 163
 
8.6%
S 160
 
8.4%
I 135
 
7.1%
R 104
 
5.5%
D 86
 
4.5%
F 78
 
4.1%
B 77
 
4.0%
K 75
 
3.9%
Other values (12) 495
26.0%

TIME_PERIOD
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.7114
Minimum1970
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:41.996336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1982
Q11995
median2005
Q32014
95-th percentile2021
Maximum2022
Range52
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.8875
Coefficient of variation (CV)0.0059327404
Kurtosis-0.73517764
Mean2003.7114
Median Absolute Deviation (MAD)9
Skewness-0.36585019
Sum1909537
Variance141.31265
MonotonicityNot monotonic
2024-01-10T02:03:42.137391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 27
 
2.8%
2007 27
 
2.8%
2015 27
 
2.8%
2014 27
 
2.8%
2013 27
 
2.8%
2012 27
 
2.8%
2011 27
 
2.8%
2010 27
 
2.8%
2009 27
 
2.8%
2008 27
 
2.8%
Other values (43) 683
71.7%
ValueCountFrequency (%)
1970 1
 
0.1%
1971 1
 
0.1%
1972 1
 
0.1%
1973 1
 
0.1%
1974 1
 
0.1%
1975 1
 
0.1%
1976 1
 
0.1%
1977 1
 
0.1%
1978 1
 
0.1%
1979 11
1.2%
ValueCountFrequency (%)
2022 27
2.8%
2021 27
2.8%
2020 27
2.8%
2019 27
2.8%
2018 27
2.8%
2017 27
2.8%
2016 27
2.8%
2015 27
2.8%
2014 27
2.8%
2013 27
2.8%

ESTAT:ILC_LI02(1.0)_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct145
Distinct (%)23.6%
Missing338
Missing (%)35.5%
Infinite0
Infinite (%)0.0%
Mean16.02
Minimum8
Maximum26.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:42.278758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile10
Q112.95
median15.5
Q319.4
95-th percentile22.36
Maximum26.4
Range18.4
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation3.9336608
Coefficient of variation (CV)0.24554687
Kurtosis-0.86962146
Mean16.02
Median Absolute Deviation (MAD)3.2
Skewness0.20041356
Sum9852.3
Variance15.473687
MonotonicityNot monotonic
2024-01-10T02:03:42.428353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 22
 
2.3%
12 15
 
1.6%
19 13
 
1.4%
20 13
 
1.4%
15 12
 
1.3%
18 12
 
1.3%
21 12
 
1.3%
13 12
 
1.3%
16 10
 
1.0%
14.8 9
 
0.9%
Other values (135) 485
50.9%
(Missing) 338
35.5%
ValueCountFrequency (%)
8 5
0.5%
8.6 3
 
0.3%
9 4
0.4%
9.1 1
 
0.1%
9.5 2
 
0.2%
9.6 3
 
0.3%
9.7 4
0.4%
9.8 1
 
0.1%
9.9 1
 
0.1%
10 9
0.9%
ValueCountFrequency (%)
26.4 1
 
0.1%
25.9 1
 
0.1%
25.4 1
 
0.1%
25.3 1
 
0.1%
25.1 1
 
0.1%
24.6 1
 
0.1%
23.8 2
0.2%
23.6 2
0.2%
23.5 2
0.2%
23.4 3
0.3%

OBS_FLAG
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.4%
Missing912
Missing (%)95.7%
Memory size7.6 KiB
b
41 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb
2nd rowb
3rd rowb
4th rowb
5th rowb

Common Values

ValueCountFrequency (%)
b 41
 
4.3%
(Missing) 912
95.7%

Length

2024-01-10T02:03:42.547202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:42.670844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
b 41
100.0%

Most occurring characters

ValueCountFrequency (%)
b 41
100.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 41
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 41
100.0%

DATAFLOW_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
ESTAT:ILC_PW01(1.0)
27 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters513
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_PW01(1.0)
2nd rowESTAT:ILC_PW01(1.0)
3rd rowESTAT:ILC_PW01(1.0)
4th rowESTAT:ILC_PW01(1.0)
5th rowESTAT:ILC_PW01(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_PW01(1.0) 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:42.765047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:42.859531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_pw01(1.0 27
100.0%

Most occurring characters

ValueCountFrequency (%)
T 54
 
10.5%
0 54
 
10.5%
1 54
 
10.5%
E 27
 
5.3%
S 27
 
5.3%
A 27
 
5.3%
: 27
 
5.3%
I 27
 
5.3%
L 27
 
5.3%
C 27
 
5.3%
Other values (6) 162
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 270
52.6%
Decimal Number 108
 
21.1%
Other Punctuation 54
 
10.5%
Connector Punctuation 27
 
5.3%
Open Punctuation 27
 
5.3%
Close Punctuation 27
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 54
20.0%
E 27
10.0%
S 27
10.0%
A 27
10.0%
I 27
10.0%
L 27
10.0%
C 27
10.0%
P 27
10.0%
W 27
10.0%
Decimal Number
ValueCountFrequency (%)
0 54
50.0%
1 54
50.0%
Other Punctuation
ValueCountFrequency (%)
: 27
50.0%
. 27
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270
52.6%
Common 243
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 54
20.0%
E 27
10.0%
S 27
10.0%
A 27
10.0%
I 27
10.0%
L 27
10.0%
C 27
10.0%
P 27
10.0%
W 27
10.0%
Common
ValueCountFrequency (%)
0 54
22.2%
1 54
22.2%
: 27
11.1%
_ 27
11.1%
( 27
11.1%
. 27
11.1%
) 27
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 54
 
10.5%
0 54
 
10.5%
1 54
 
10.5%
E 27
 
5.3%
S 27
 
5.3%
A 27
 
5.3%
: 27
 
5.3%
I 27
 
5.3%
L 27
 
5.3%
C 27
 
5.3%
Other values (6) 162
31.6%

LAST UPDATE_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
15/12/23 23:00:00
27 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters459
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:42.956846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:43.063339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 27
50.0%
23:00:00 27
50.0%

Most occurring characters

ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 324
70.6%
Other Punctuation 108
 
23.5%
Space Separator 27
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 108
33.3%
2 81
25.0%
1 54
16.7%
3 54
16.7%
5 27
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 54
50.0%
: 54
50.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

freq_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
A
27 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:43.144027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:43.254074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 27
100.0%

Most occurring characters

ValueCountFrequency (%)
A 27
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 27
100.0%

ESTAT:ILC_PW01(1.0)_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
RTG
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:43.346650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:43.442253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 27
100.0%

Most occurring characters

ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

isced11
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
TOTAL
27 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters135
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:43.533880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:43.644015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 27
100.0%

Most occurring characters

ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 135
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 135
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

indic_wb
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
ACCSAT
27 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters162
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACCSAT
2nd rowACCSAT
3rd rowACCSAT
4th rowACCSAT
5th rowACCSAT

Common Values

ValueCountFrequency (%)
ACCSAT 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:43.722568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:43.832798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
accsat 27
100.0%

Most occurring characters

ValueCountFrequency (%)
A 54
33.3%
C 54
33.3%
S 27
16.7%
T 27
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 54
33.3%
C 54
33.3%
S 27
16.7%
T 27
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 54
33.3%
C 54
33.3%
S 27
16.7%
T 27
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 54
33.3%
C 54
33.3%
S 27
16.7%
T 27
16.7%

sex_1
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size2.0 KiB
True
 
27
(Missing)
926 
ValueCountFrequency (%)
True 27
 
2.8%
(Missing) 926
97.2%
2024-01-10T02:03:43.926927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
Y_GE16
27 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters162
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_GE16
2nd rowY_GE16
3rd rowY_GE16
4th rowY_GE16
5th rowY_GE16

Common Values

ValueCountFrequency (%)
Y_GE16 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:44.005503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:44.115231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y_ge16 27
100.0%

Most occurring characters

ValueCountFrequency (%)
Y 27
16.7%
_ 27
16.7%
G 27
16.7%
E 27
16.7%
1 27
16.7%
6 27
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
50.0%
Decimal Number 54
33.3%
Connector Punctuation 27
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 27
33.3%
G 27
33.3%
E 27
33.3%
Decimal Number
ValueCountFrequency (%)
1 27
50.0%
6 27
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
50.0%
Common 81
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 27
33.3%
G 27
33.3%
E 27
33.3%
Common
ValueCountFrequency (%)
_ 27
33.3%
1 27
33.3%
6 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 27
16.7%
_ 27
16.7%
G 27
16.7%
E 27
16.7%
1 27
16.7%
6 27
16.7%

ESTAT:ILC_PW01(1.0)_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)66.7%
Missing926
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean7.5
Minimum6
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:44.193799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.6
Q17.25
median7.5
Q37.85
95-th percentile8.37
Maximum8.4
Range2.4
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.58441292
Coefficient of variation (CV)0.077921723
Kurtosis0.35106647
Mean7.5
Median Absolute Deviation (MAD)0.3
Skewness-0.56439176
Sum202.5
Variance0.34153846
MonotonicityNot monotonic
2024-01-10T02:03:44.303457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
7.4 3
 
0.3%
7.5 2
 
0.2%
6.6 2
 
0.2%
8.4 2
 
0.2%
7.3 2
 
0.2%
7.7 2
 
0.2%
7.8 2
 
0.2%
7.6 2
 
0.2%
7.1 1
 
0.1%
6 1
 
0.1%
Other values (8) 8
 
0.8%
(Missing) 926
97.2%
ValueCountFrequency (%)
6 1
 
0.1%
6.6 2
0.2%
6.8 1
 
0.1%
6.9 1
 
0.1%
7.1 1
 
0.1%
7.2 1
 
0.1%
7.3 2
0.2%
7.4 3
0.3%
7.5 2
0.2%
7.6 2
0.2%
ValueCountFrequency (%)
8.4 2
0.2%
8.3 1
0.1%
8.2 1
0.1%
8.1 1
0.1%
8 1
0.1%
7.9 1
0.1%
7.8 2
0.2%
7.7 2
0.2%
7.6 2
0.2%
7.5 2
0.2%

OBS_FLAG_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing953
Missing (%)100.0%
Memory size7.6 KiB

DATAFLOW_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
ESTAT:ILC_PW01(1.0)
27 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters513
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_PW01(1.0)
2nd rowESTAT:ILC_PW01(1.0)
3rd rowESTAT:ILC_PW01(1.0)
4th rowESTAT:ILC_PW01(1.0)
5th rowESTAT:ILC_PW01(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_PW01(1.0) 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:44.429193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:44.523406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_pw01(1.0 27
100.0%

Most occurring characters

ValueCountFrequency (%)
T 54
 
10.5%
0 54
 
10.5%
1 54
 
10.5%
E 27
 
5.3%
S 27
 
5.3%
A 27
 
5.3%
: 27
 
5.3%
I 27
 
5.3%
L 27
 
5.3%
C 27
 
5.3%
Other values (6) 162
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 270
52.6%
Decimal Number 108
 
21.1%
Other Punctuation 54
 
10.5%
Connector Punctuation 27
 
5.3%
Open Punctuation 27
 
5.3%
Close Punctuation 27
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 54
20.0%
E 27
10.0%
S 27
10.0%
A 27
10.0%
I 27
10.0%
L 27
10.0%
C 27
10.0%
P 27
10.0%
W 27
10.0%
Decimal Number
ValueCountFrequency (%)
0 54
50.0%
1 54
50.0%
Other Punctuation
ValueCountFrequency (%)
: 27
50.0%
. 27
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270
52.6%
Common 243
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 54
20.0%
E 27
10.0%
S 27
10.0%
A 27
10.0%
I 27
10.0%
L 27
10.0%
C 27
10.0%
P 27
10.0%
W 27
10.0%
Common
ValueCountFrequency (%)
0 54
22.2%
1 54
22.2%
: 27
11.1%
_ 27
11.1%
( 27
11.1%
. 27
11.1%
) 27
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 54
 
10.5%
0 54
 
10.5%
1 54
 
10.5%
E 27
 
5.3%
S 27
 
5.3%
A 27
 
5.3%
: 27
 
5.3%
I 27
 
5.3%
L 27
 
5.3%
C 27
 
5.3%
Other values (6) 162
31.6%

LAST UPDATE_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
15/12/23 23:00:00
27 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters459
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:44.633007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:44.727509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 27
50.0%
23:00:00 27
50.0%

Most occurring characters

ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 324
70.6%
Other Punctuation 108
 
23.5%
Space Separator 27
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 108
33.3%
2 81
25.0%
1 54
16.7%
3 54
16.7%
5 27
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 54
50.0%
: 54
50.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

freq_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
A
27 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:44.822235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:44.916181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 27
100.0%

Most occurring characters

ValueCountFrequency (%)
A 27
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 27
100.0%

ESTAT:ILC_PW01(1.0)_unit_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
RTG
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:45.010367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:45.120037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 27
100.0%

Most occurring characters

ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

isced11_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
TOTAL
27 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters135
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:45.198629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:45.308290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 27
100.0%

Most occurring characters

ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 135
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 135
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

indic_wb_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
COMSAT
27 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters162
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMSAT
2nd rowCOMSAT
3rd rowCOMSAT
4th rowCOMSAT
5th rowCOMSAT

Common Values

ValueCountFrequency (%)
COMSAT 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:45.387263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:45.496962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
comsat 27
100.0%

Most occurring characters

ValueCountFrequency (%)
C 27
16.7%
O 27
16.7%
M 27
16.7%
S 27
16.7%
A 27
16.7%
T 27
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 27
16.7%
O 27
16.7%
M 27
16.7%
S 27
16.7%
A 27
16.7%
T 27
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 27
16.7%
O 27
16.7%
M 27
16.7%
S 27
16.7%
A 27
16.7%
T 27
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 27
16.7%
O 27
16.7%
M 27
16.7%
S 27
16.7%
A 27
16.7%
T 27
16.7%

sex_2
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size2.0 KiB
True
 
27
(Missing)
926 
ValueCountFrequency (%)
True 27
 
2.8%
(Missing) 926
97.2%
2024-01-10T02:03:45.591154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
Y_GE16
27 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters162
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_GE16
2nd rowY_GE16
3rd rowY_GE16
4th rowY_GE16
5th rowY_GE16

Common Values

ValueCountFrequency (%)
Y_GE16 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:45.670182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:45.779812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y_ge16 27
100.0%

Most occurring characters

ValueCountFrequency (%)
Y 27
16.7%
_ 27
16.7%
G 27
16.7%
E 27
16.7%
1 27
16.7%
6 27
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
50.0%
Decimal Number 54
33.3%
Connector Punctuation 27
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 27
33.3%
G 27
33.3%
E 27
33.3%
Decimal Number
ValueCountFrequency (%)
1 27
50.0%
6 27
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
50.0%
Common 81
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 27
33.3%
G 27
33.3%
E 27
33.3%
Common
ValueCountFrequency (%)
_ 27
33.3%
1 27
33.3%
6 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 27
16.7%
_ 27
16.7%
G 27
16.7%
E 27
16.7%
1 27
16.7%
6 27
16.7%

ESTAT:ILC_PW01(1.0)_VALUE_2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)59.3%
Missing926
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean7.4222222
Minimum5.9
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:45.858773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile6.53
Q17.15
median7.5
Q37.75
95-th percentile8.14
Maximum8.3
Range2.4
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.53445395
Coefficient of variation (CV)0.072007269
Kurtosis1.3708107
Mean7.4222222
Median Absolute Deviation (MAD)0.3
Skewness-0.85149409
Sum200.4
Variance0.28564103
MonotonicityNot monotonic
2024-01-10T02:03:45.968370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.5 5
 
0.5%
8 3
 
0.3%
7.1 3
 
0.3%
7.2 2
 
0.2%
7.3 2
 
0.2%
7.7 2
 
0.2%
7.9 1
 
0.1%
6.6 1
 
0.1%
7 1
 
0.1%
6.5 1
 
0.1%
Other values (6) 6
 
0.6%
(Missing) 926
97.2%
ValueCountFrequency (%)
5.9 1
 
0.1%
6.5 1
 
0.1%
6.6 1
 
0.1%
7 1
 
0.1%
7.1 3
0.3%
7.2 2
 
0.2%
7.3 2
 
0.2%
7.4 1
 
0.1%
7.5 5
0.5%
7.6 1
 
0.1%
ValueCountFrequency (%)
8.3 1
 
0.1%
8.2 1
 
0.1%
8 3
0.3%
7.9 1
 
0.1%
7.8 1
 
0.1%
7.7 2
 
0.2%
7.6 1
 
0.1%
7.5 5
0.5%
7.4 1
 
0.1%
7.3 2
 
0.2%

OBS_FLAG_2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing953
Missing (%)100.0%
Memory size7.6 KiB

DATAFLOW_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
ESTAT:ILC_PW01(1.0)
54 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1026
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_PW01(1.0)
2nd rowESTAT:ILC_PW01(1.0)
3rd rowESTAT:ILC_PW01(1.0)
4th rowESTAT:ILC_PW01(1.0)
5th rowESTAT:ILC_PW01(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_PW01(1.0) 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:46.078180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:46.187848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_pw01(1.0 54
100.0%

Most occurring characters

ValueCountFrequency (%)
T 108
 
10.5%
0 108
 
10.5%
1 108
 
10.5%
E 54
 
5.3%
S 54
 
5.3%
A 54
 
5.3%
: 54
 
5.3%
I 54
 
5.3%
L 54
 
5.3%
C 54
 
5.3%
Other values (6) 324
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 540
52.6%
Decimal Number 216
 
21.1%
Other Punctuation 108
 
10.5%
Connector Punctuation 54
 
5.3%
Open Punctuation 54
 
5.3%
Close Punctuation 54
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 108
20.0%
E 54
10.0%
S 54
10.0%
A 54
10.0%
I 54
10.0%
L 54
10.0%
C 54
10.0%
P 54
10.0%
W 54
10.0%
Decimal Number
ValueCountFrequency (%)
0 108
50.0%
1 108
50.0%
Other Punctuation
ValueCountFrequency (%)
: 54
50.0%
. 54
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 540
52.6%
Common 486
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 108
20.0%
E 54
10.0%
S 54
10.0%
A 54
10.0%
I 54
10.0%
L 54
10.0%
C 54
10.0%
P 54
10.0%
W 54
10.0%
Common
ValueCountFrequency (%)
0 108
22.2%
1 108
22.2%
: 54
11.1%
_ 54
11.1%
( 54
11.1%
. 54
11.1%
) 54
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 108
 
10.5%
0 108
 
10.5%
1 108
 
10.5%
E 54
 
5.3%
S 54
 
5.3%
A 54
 
5.3%
: 54
 
5.3%
I 54
 
5.3%
L 54
 
5.3%
C 54
 
5.3%
Other values (6) 324
31.6%

LAST UPDATE_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
15/12/23 23:00:00
54 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters918
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:46.282299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:46.376718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 54
50.0%
23:00:00 54
50.0%

Most occurring characters

ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 648
70.6%
Other Punctuation 216
 
23.5%
Space Separator 54
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 216
33.3%
2 162
25.0%
1 108
16.7%
3 108
16.7%
5 54
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 108
50.0%
: 108
50.0%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 918
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

freq_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
A
54 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:46.470913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:46.580586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 54
100.0%

Most occurring characters

ValueCountFrequency (%)
A 54
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 54
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 54
100.0%

ESTAT:ILC_PW01(1.0)_unit_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
RTG
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters162
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:46.659388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:46.769216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 54
100.0%

Most occurring characters

ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

isced11_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
TOTAL
54 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters270
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:46.850697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:46.957896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 54
100.0%

Most occurring characters

ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 270
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

indic_wb_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
FINSAT
54 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters324
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFINSAT
2nd rowFINSAT
3rd rowFINSAT
4th rowFINSAT
5th rowFINSAT

Common Values

ValueCountFrequency (%)
FINSAT 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:47.054558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:47.148242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
finsat 54
100.0%

Most occurring characters

ValueCountFrequency (%)
F 54
16.7%
I 54
16.7%
N 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 324
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 54
16.7%
I 54
16.7%
N 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 324
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 54
16.7%
I 54
16.7%
N 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 54
16.7%
I 54
16.7%
N 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

sex_3
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size2.0 KiB
True
 
54
(Missing)
899 
ValueCountFrequency (%)
True 54
 
5.7%
(Missing) 899
94.3%
2024-01-10T02:03:47.242769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
Y_GE16
54 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters324
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_GE16
2nd rowY_GE16
3rd rowY_GE16
4th rowY_GE16
5th rowY_GE16

Common Values

ValueCountFrequency (%)
Y_GE16 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:47.334307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:47.439195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y_ge16 54
100.0%

Most occurring characters

ValueCountFrequency (%)
Y 54
16.7%
_ 54
16.7%
G 54
16.7%
E 54
16.7%
1 54
16.7%
6 54
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
50.0%
Decimal Number 108
33.3%
Connector Punctuation 54
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 54
33.3%
G 54
33.3%
E 54
33.3%
Decimal Number
ValueCountFrequency (%)
1 54
50.0%
6 54
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
50.0%
Common 162
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 54
33.3%
G 54
33.3%
E 54
33.3%
Common
ValueCountFrequency (%)
_ 54
33.3%
1 54
33.3%
6 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 54
16.7%
_ 54
16.7%
G 54
16.7%
E 54
16.7%
1 54
16.7%
6 54
16.7%

ESTAT:ILC_PW01(1.0)_VALUE_3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)50.0%
Missing899
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean6.1259259
Minimum3.7
Maximum7.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:47.518701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile4.43
Q15.425
median6.15
Q36.9
95-th percentile7.6
Maximum7.6
Range3.9
Interquartile range (IQR)1.475

Descriptive statistics

Standard deviation0.97442488
Coefficient of variation (CV)0.15906573
Kurtosis-0.51933199
Mean6.1259259
Median Absolute Deviation (MAD)0.75
Skewness-0.27892564
Sum330.8
Variance0.94950384
MonotonicityNot monotonic
2024-01-10T02:03:47.647371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5.2 5
 
0.5%
7.6 4
 
0.4%
6.3 4
 
0.4%
5.5 3
 
0.3%
6.9 3
 
0.3%
6 3
 
0.3%
5.4 3
 
0.3%
6.4 2
 
0.2%
7.4 2
 
0.2%
5.7 2
 
0.2%
Other values (17) 23
 
2.4%
(Missing) 899
94.3%
ValueCountFrequency (%)
3.7 1
 
0.1%
4.3 2
 
0.2%
4.5 1
 
0.1%
4.6 1
 
0.1%
5 1
 
0.1%
5.2 5
0.5%
5.4 3
0.3%
5.5 3
0.3%
5.6 1
 
0.1%
5.7 2
 
0.2%
ValueCountFrequency (%)
7.6 4
0.4%
7.5 2
0.2%
7.4 2
0.2%
7.3 1
 
0.1%
7.2 1
 
0.1%
7 2
0.2%
6.9 3
0.3%
6.8 2
0.2%
6.7 1
 
0.1%
6.6 2
0.2%

OBS_FLAG_3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing953
Missing (%)100.0%
Memory size7.6 KiB

DATAFLOW_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
ESTAT:ILC_PW01(1.0)
27 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters513
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_PW01(1.0)
2nd rowESTAT:ILC_PW01(1.0)
3rd rowESTAT:ILC_PW01(1.0)
4th rowESTAT:ILC_PW01(1.0)
5th rowESTAT:ILC_PW01(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_PW01(1.0) 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:48.354068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:48.461253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_pw01(1.0 27
100.0%

Most occurring characters

ValueCountFrequency (%)
T 54
 
10.5%
0 54
 
10.5%
1 54
 
10.5%
E 27
 
5.3%
S 27
 
5.3%
A 27
 
5.3%
: 27
 
5.3%
I 27
 
5.3%
L 27
 
5.3%
C 27
 
5.3%
Other values (6) 162
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 270
52.6%
Decimal Number 108
 
21.1%
Other Punctuation 54
 
10.5%
Connector Punctuation 27
 
5.3%
Open Punctuation 27
 
5.3%
Close Punctuation 27
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 54
20.0%
E 27
10.0%
S 27
10.0%
A 27
10.0%
I 27
10.0%
L 27
10.0%
C 27
10.0%
P 27
10.0%
W 27
10.0%
Decimal Number
ValueCountFrequency (%)
0 54
50.0%
1 54
50.0%
Other Punctuation
ValueCountFrequency (%)
: 27
50.0%
. 27
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270
52.6%
Common 243
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 54
20.0%
E 27
10.0%
S 27
10.0%
A 27
10.0%
I 27
10.0%
L 27
10.0%
C 27
10.0%
P 27
10.0%
W 27
10.0%
Common
ValueCountFrequency (%)
0 54
22.2%
1 54
22.2%
: 27
11.1%
_ 27
11.1%
( 27
11.1%
. 27
11.1%
) 27
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 54
 
10.5%
0 54
 
10.5%
1 54
 
10.5%
E 27
 
5.3%
S 27
 
5.3%
A 27
 
5.3%
: 27
 
5.3%
I 27
 
5.3%
L 27
 
5.3%
C 27
 
5.3%
Other values (6) 162
31.6%

LAST UPDATE_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
15/12/23 23:00:00
27 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters459
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:48.542322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:48.652306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 27
50.0%
23:00:00 27
50.0%

Most occurring characters

ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 324
70.6%
Other Punctuation 108
 
23.5%
Space Separator 27
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 108
33.3%
2 81
25.0%
1 54
16.7%
3 54
16.7%
5 27
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 54
50.0%
: 54
50.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

freq_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
A
27 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:48.746008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:48.841023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 27
100.0%

Most occurring characters

ValueCountFrequency (%)
A 27
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 27
100.0%

ESTAT:ILC_PW01(1.0)_unit_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
RTG
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:48.932152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:49.044385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 27
100.0%

Most occurring characters

ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

isced11_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
TOTAL
27 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters135
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:49.120390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:49.230197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 27
100.0%

Most occurring characters

ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 135
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 135
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

indic_wb_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
GREENSAT
27 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters216
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGREENSAT
2nd rowGREENSAT
3rd rowGREENSAT
4th rowGREENSAT
5th rowGREENSAT

Common Values

ValueCountFrequency (%)
GREENSAT 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:49.308595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:49.418850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
greensat 27
100.0%

Most occurring characters

ValueCountFrequency (%)
E 54
25.0%
G 27
12.5%
R 27
12.5%
N 27
12.5%
S 27
12.5%
A 27
12.5%
T 27
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 216
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 54
25.0%
G 27
12.5%
R 27
12.5%
N 27
12.5%
S 27
12.5%
A 27
12.5%
T 27
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 216
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 54
25.0%
G 27
12.5%
R 27
12.5%
N 27
12.5%
S 27
12.5%
A 27
12.5%
T 27
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 54
25.0%
G 27
12.5%
R 27
12.5%
N 27
12.5%
S 27
12.5%
A 27
12.5%
T 27
12.5%

sex_4
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size2.0 KiB
True
 
27
(Missing)
926 
ValueCountFrequency (%)
True 27
 
2.8%
(Missing) 926
97.2%
2024-01-10T02:03:49.512959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
Y_GE16
27 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters162
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_GE16
2nd rowY_GE16
3rd rowY_GE16
4th rowY_GE16
5th rowY_GE16

Common Values

ValueCountFrequency (%)
Y_GE16 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:49.591542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:49.701602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y_ge16 27
100.0%

Most occurring characters

ValueCountFrequency (%)
Y 27
16.7%
_ 27
16.7%
G 27
16.7%
E 27
16.7%
1 27
16.7%
6 27
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
50.0%
Decimal Number 54
33.3%
Connector Punctuation 27
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 27
33.3%
G 27
33.3%
E 27
33.3%
Decimal Number
ValueCountFrequency (%)
1 27
50.0%
6 27
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
50.0%
Common 81
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 27
33.3%
G 27
33.3%
E 27
33.3%
Common
ValueCountFrequency (%)
_ 27
33.3%
1 27
33.3%
6 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 27
16.7%
_ 27
16.7%
G 27
16.7%
E 27
16.7%
1 27
16.7%
6 27
16.7%

ESTAT:ILC_PW01(1.0)_VALUE_4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)70.4%
Missing926
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean7.1
Minimum5.2
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:49.780167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile5.83
Q16.4
median7.3
Q37.7
95-th percentile8.3
Maximum8.4
Range3.2
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation0.89828897
Coefficient of variation (CV)0.12651957
Kurtosis-0.83531185
Mean7.1
Median Absolute Deviation (MAD)0.7
Skewness-0.35518431
Sum191.7
Variance0.80692308
MonotonicityNot monotonic
2024-01-10T02:03:49.890316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7.5 4
 
0.4%
8.3 3
 
0.3%
5.9 2
 
0.2%
7 2
 
0.2%
6.6 2
 
0.2%
7.4 1
 
0.1%
7.9 1
 
0.1%
8.4 1
 
0.1%
6 1
 
0.1%
8.1 1
 
0.1%
Other values (9) 9
 
0.9%
(Missing) 926
97.2%
ValueCountFrequency (%)
5.2 1
0.1%
5.8 1
0.1%
5.9 2
0.2%
6 1
0.1%
6.1 1
0.1%
6.2 1
0.1%
6.6 2
0.2%
6.8 1
0.1%
7 2
0.2%
7.2 1
0.1%
ValueCountFrequency (%)
8.4 1
 
0.1%
8.3 3
0.3%
8.1 1
 
0.1%
7.9 1
 
0.1%
7.8 1
 
0.1%
7.6 1
 
0.1%
7.5 4
0.4%
7.4 1
 
0.1%
7.3 1
 
0.1%
7.2 1
 
0.1%

OBS_FLAG_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing953
Missing (%)100.0%
Memory size7.6 KiB

DATAFLOW_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
ESTAT:ILC_PW01(1.0)
54 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1026
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_PW01(1.0)
2nd rowESTAT:ILC_PW01(1.0)
3rd rowESTAT:ILC_PW01(1.0)
4th rowESTAT:ILC_PW01(1.0)
5th rowESTAT:ILC_PW01(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_PW01(1.0) 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:50.000139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:50.109845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_pw01(1.0 54
100.0%

Most occurring characters

ValueCountFrequency (%)
T 108
 
10.5%
0 108
 
10.5%
1 108
 
10.5%
E 54
 
5.3%
S 54
 
5.3%
A 54
 
5.3%
: 54
 
5.3%
I 54
 
5.3%
L 54
 
5.3%
C 54
 
5.3%
Other values (6) 324
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 540
52.6%
Decimal Number 216
 
21.1%
Other Punctuation 108
 
10.5%
Connector Punctuation 54
 
5.3%
Open Punctuation 54
 
5.3%
Close Punctuation 54
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 108
20.0%
E 54
10.0%
S 54
10.0%
A 54
10.0%
I 54
10.0%
L 54
10.0%
C 54
10.0%
P 54
10.0%
W 54
10.0%
Decimal Number
ValueCountFrequency (%)
0 108
50.0%
1 108
50.0%
Other Punctuation
ValueCountFrequency (%)
: 54
50.0%
. 54
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 540
52.6%
Common 486
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 108
20.0%
E 54
10.0%
S 54
10.0%
A 54
10.0%
I 54
10.0%
L 54
10.0%
C 54
10.0%
P 54
10.0%
W 54
10.0%
Common
ValueCountFrequency (%)
0 108
22.2%
1 108
22.2%
: 54
11.1%
_ 54
11.1%
( 54
11.1%
. 54
11.1%
) 54
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 108
 
10.5%
0 108
 
10.5%
1 108
 
10.5%
E 54
 
5.3%
S 54
 
5.3%
A 54
 
5.3%
: 54
 
5.3%
I 54
 
5.3%
L 54
 
5.3%
C 54
 
5.3%
Other values (6) 324
31.6%

LAST UPDATE_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
15/12/23 23:00:00
54 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters918
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:50.204029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:50.314180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 54
50.0%
23:00:00 54
50.0%

Most occurring characters

ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 648
70.6%
Other Punctuation 216
 
23.5%
Space Separator 54
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 216
33.3%
2 162
25.0%
1 108
16.7%
3 108
16.7%
5 54
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 108
50.0%
: 108
50.0%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 918
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

freq_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
A
54 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:50.392471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:50.502302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 54
100.0%

Most occurring characters

ValueCountFrequency (%)
A 54
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 54
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 54
100.0%

ESTAT:ILC_PW01(1.0)_unit_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
RTG
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters162
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:50.580769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:50.690998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 54
100.0%

Most occurring characters

ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

isced11_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
TOTAL
54 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters270
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:50.784874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:50.879475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 54
100.0%

Most occurring characters

ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 270
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

indic_wb_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
JOBSAT
54 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters324
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJOBSAT
2nd rowJOBSAT
3rd rowJOBSAT
4th rowJOBSAT
5th rowJOBSAT

Common Values

ValueCountFrequency (%)
JOBSAT 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:50.973522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:51.083349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
jobsat 54
100.0%

Most occurring characters

ValueCountFrequency (%)
J 54
16.7%
O 54
16.7%
B 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 324
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J 54
16.7%
O 54
16.7%
B 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 324
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 54
16.7%
O 54
16.7%
B 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
J 54
16.7%
O 54
16.7%
B 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

sex_5
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size2.0 KiB
True
 
54
(Missing)
899 
ValueCountFrequency (%)
True 54
 
5.7%
(Missing) 899
94.3%
2024-01-10T02:03:51.161798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
Y_GE16
54 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters324
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_GE16
2nd rowY_GE16
3rd rowY_GE16
4th rowY_GE16
5th rowY_GE16

Common Values

ValueCountFrequency (%)
Y_GE16 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:51.256328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:51.366532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y_ge16 54
100.0%

Most occurring characters

ValueCountFrequency (%)
Y 54
16.7%
_ 54
16.7%
G 54
16.7%
E 54
16.7%
1 54
16.7%
6 54
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
50.0%
Decimal Number 108
33.3%
Connector Punctuation 54
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 54
33.3%
G 54
33.3%
E 54
33.3%
Decimal Number
ValueCountFrequency (%)
1 54
50.0%
6 54
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
50.0%
Common 162
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 54
33.3%
G 54
33.3%
E 54
33.3%
Common
ValueCountFrequency (%)
_ 54
33.3%
1 54
33.3%
6 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 54
16.7%
_ 54
16.7%
G 54
16.7%
E 54
16.7%
1 54
16.7%
6 54
16.7%

ESTAT:ILC_PW01(1.0)_VALUE_5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)29.6%
Missing899
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean7.2851852
Minimum6
Maximum8.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:51.447599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.33
Q17.1
median7.3
Q37.5
95-th percentile8.035
Maximum8.1
Range2.1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.44442348
Coefficient of variation (CV)0.061003731
Kurtosis1.609569
Mean7.2851852
Median Absolute Deviation (MAD)0.2
Skewness-0.7175413
Sum393.4
Variance0.19751223
MonotonicityNot monotonic
2024-01-10T02:03:51.554799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.3 9
 
0.9%
7.5 8
 
0.8%
7 7
 
0.7%
7.2 6
 
0.6%
7.1 4
 
0.4%
8.1 3
 
0.3%
7.7 3
 
0.3%
7.4 3
 
0.3%
8 2
 
0.2%
7.6 2
 
0.2%
Other values (6) 7
 
0.7%
(Missing) 899
94.3%
ValueCountFrequency (%)
6 1
 
0.1%
6.1 1
 
0.1%
6.2 1
 
0.1%
6.4 1
 
0.1%
6.9 2
 
0.2%
7 7
0.7%
7.1 4
0.4%
7.2 6
0.6%
7.3 9
0.9%
7.4 3
 
0.3%
ValueCountFrequency (%)
8.1 3
 
0.3%
8 2
 
0.2%
7.8 1
 
0.1%
7.7 3
 
0.3%
7.6 2
 
0.2%
7.5 8
0.8%
7.4 3
 
0.3%
7.3 9
0.9%
7.2 6
0.6%
7.1 4
0.4%

OBS_FLAG_5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing953
Missing (%)100.0%
Memory size7.6 KiB

DATAFLOW_6
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing845
Missing (%)88.7%
Memory size7.6 KiB
ESTAT:ILC_PW01(1.0)
108 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2052
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_PW01(1.0)
2nd rowESTAT:ILC_PW01(1.0)
3rd rowESTAT:ILC_PW01(1.0)
4th rowESTAT:ILC_PW01(1.0)
5th rowESTAT:ILC_PW01(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_PW01(1.0) 108
 
11.3%
(Missing) 845
88.7%

Length

2024-01-10T02:03:51.664877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:51.774702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_pw01(1.0 108
100.0%

Most occurring characters

ValueCountFrequency (%)
T 216
 
10.5%
0 216
 
10.5%
1 216
 
10.5%
E 108
 
5.3%
S 108
 
5.3%
A 108
 
5.3%
: 108
 
5.3%
I 108
 
5.3%
L 108
 
5.3%
C 108
 
5.3%
Other values (6) 648
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1080
52.6%
Decimal Number 432
 
21.1%
Other Punctuation 216
 
10.5%
Connector Punctuation 108
 
5.3%
Open Punctuation 108
 
5.3%
Close Punctuation 108
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 216
20.0%
E 108
10.0%
S 108
10.0%
A 108
10.0%
I 108
10.0%
L 108
10.0%
C 108
10.0%
P 108
10.0%
W 108
10.0%
Decimal Number
ValueCountFrequency (%)
0 216
50.0%
1 216
50.0%
Other Punctuation
ValueCountFrequency (%)
: 108
50.0%
. 108
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 108
100.0%
Open Punctuation
ValueCountFrequency (%)
( 108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1080
52.6%
Common 972
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 216
20.0%
E 108
10.0%
S 108
10.0%
A 108
10.0%
I 108
10.0%
L 108
10.0%
C 108
10.0%
P 108
10.0%
W 108
10.0%
Common
ValueCountFrequency (%)
0 216
22.2%
1 216
22.2%
: 108
11.1%
_ 108
11.1%
( 108
11.1%
. 108
11.1%
) 108
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 216
 
10.5%
0 216
 
10.5%
1 216
 
10.5%
E 108
 
5.3%
S 108
 
5.3%
A 108
 
5.3%
: 108
 
5.3%
I 108
 
5.3%
L 108
 
5.3%
C 108
 
5.3%
Other values (6) 648
31.6%

LAST UPDATE_6
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing845
Missing (%)88.7%
Memory size7.6 KiB
15/12/23 23:00:00
108 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters1836
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 108
 
11.3%
(Missing) 845
88.7%

Length

2024-01-10T02:03:51.857284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:51.963503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 108
50.0%
23:00:00 108
50.0%

Most occurring characters

ValueCountFrequency (%)
0 432
23.5%
2 324
17.6%
1 216
11.8%
/ 216
11.8%
3 216
11.8%
: 216
11.8%
5 108
 
5.9%
108
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296
70.6%
Other Punctuation 432
 
23.5%
Space Separator 108
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 432
33.3%
2 324
25.0%
1 216
16.7%
3 216
16.7%
5 108
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 216
50.0%
: 216
50.0%
Space Separator
ValueCountFrequency (%)
108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1836
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 432
23.5%
2 324
17.6%
1 216
11.8%
/ 216
11.8%
3 216
11.8%
: 216
11.8%
5 108
 
5.9%
108
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 432
23.5%
2 324
17.6%
1 216
11.8%
/ 216
11.8%
3 216
11.8%
: 216
11.8%
5 108
 
5.9%
108
 
5.9%

freq_6
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing845
Missing (%)88.7%
Memory size7.6 KiB
A
108 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters108
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 108
 
11.3%
(Missing) 845
88.7%

Length

2024-01-10T02:03:52.057533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:52.167348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 108
100.0%

Most occurring characters

ValueCountFrequency (%)
A 108
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 108
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 108
100.0%

ESTAT:ILC_PW01(1.0)_unit_6
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing845
Missing (%)88.7%
Memory size7.6 KiB
RTG
108 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters324
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 108
 
11.3%
(Missing) 845
88.7%

Length

2024-01-10T02:03:52.248388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:52.356032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 108
100.0%

Most occurring characters

ValueCountFrequency (%)
R 108
33.3%
T 108
33.3%
G 108
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 324
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 108
33.3%
T 108
33.3%
G 108
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 324
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 108
33.3%
T 108
33.3%
G 108
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 108
33.3%
T 108
33.3%
G 108
33.3%

isced11_6
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing845
Missing (%)88.7%
Memory size7.6 KiB
TOTAL
108 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters540
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 108
 
11.3%
(Missing) 845
88.7%

Length

2024-01-10T02:03:52.443833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:52.540699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 108
100.0%

Most occurring characters

ValueCountFrequency (%)
T 216
40.0%
O 108
20.0%
A 108
20.0%
L 108
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 540
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 216
40.0%
O 108
20.0%
A 108
20.0%
L 108
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 216
40.0%
O 108
20.0%
A 108
20.0%
L 108
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 216
40.0%
O 108
20.0%
A 108
20.0%
L 108
20.0%

indic_wb_6
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing845
Missing (%)88.7%
Memory size7.6 KiB
LIFESAT
108 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters756
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLIFESAT
2nd rowLIFESAT
3rd rowLIFESAT
4th rowLIFESAT
5th rowLIFESAT

Common Values

ValueCountFrequency (%)
LIFESAT 108
 
11.3%
(Missing) 845
88.7%

Length

2024-01-10T02:03:52.632854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:52.726788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
lifesat 108
100.0%

Most occurring characters

ValueCountFrequency (%)
L 108
14.3%
I 108
14.3%
F 108
14.3%
E 108
14.3%
S 108
14.3%
A 108
14.3%
T 108
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 756
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 108
14.3%
I 108
14.3%
F 108
14.3%
E 108
14.3%
S 108
14.3%
A 108
14.3%
T 108
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 756
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 108
14.3%
I 108
14.3%
F 108
14.3%
E 108
14.3%
S 108
14.3%
A 108
14.3%
T 108
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 108
14.3%
I 108
14.3%
F 108
14.3%
E 108
14.3%
S 108
14.3%
A 108
14.3%
T 108
14.3%

sex_6
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing845
Missing (%)88.7%
Memory size2.0 KiB
True
108 
(Missing)
845 
ValueCountFrequency (%)
True 108
 
11.3%
(Missing) 845
88.7%
2024-01-10T02:03:52.821506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age_6
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing845
Missing (%)88.7%
Memory size7.6 KiB
Y_GE16
108 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters648
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_GE16
2nd rowY_GE16
3rd rowY_GE16
4th rowY_GE16
5th rowY_GE16

Common Values

ValueCountFrequency (%)
Y_GE16 108
 
11.3%
(Missing) 845
88.7%

Length

2024-01-10T02:03:52.915482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:53.009682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y_ge16 108
100.0%

Most occurring characters

ValueCountFrequency (%)
Y 108
16.7%
_ 108
16.7%
G 108
16.7%
E 108
16.7%
1 108
16.7%
6 108
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 324
50.0%
Decimal Number 216
33.3%
Connector Punctuation 108
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 108
33.3%
G 108
33.3%
E 108
33.3%
Decimal Number
ValueCountFrequency (%)
1 108
50.0%
6 108
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 324
50.0%
Common 324
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 108
33.3%
G 108
33.3%
E 108
33.3%
Common
ValueCountFrequency (%)
_ 108
33.3%
1 108
33.3%
6 108
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 108
16.7%
_ 108
16.7%
G 108
16.7%
E 108
16.7%
1 108
16.7%
6 108
16.7%

ESTAT:ILC_PW01(1.0)_VALUE_6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)22.2%
Missing845
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean7.1314815
Minimum4.8
Maximum8.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:53.103793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.8
5-th percentile6.2
Q16.8
median7.2
Q37.5
95-th percentile7.965
Maximum8.1
Range3.3
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.58655467
Coefficient of variation (CV)0.082248643
Kurtosis2.0357311
Mean7.1314815
Median Absolute Deviation (MAD)0.35
Skewness-1.058378
Sum770.2
Variance0.34404638
MonotonicityNot monotonic
2024-01-10T02:03:53.213609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7.1 11
 
1.2%
7.3 9
 
0.9%
7 8
 
0.8%
7.5 8
 
0.8%
7.4 8
 
0.8%
7.2 7
 
0.7%
6.7 6
 
0.6%
6.8 6
 
0.6%
7.6 6
 
0.6%
7.8 5
 
0.5%
Other values (14) 34
 
3.6%
(Missing) 845
88.7%
ValueCountFrequency (%)
4.8 1
 
0.1%
5.4 1
 
0.1%
5.6 1
 
0.1%
5.7 1
 
0.1%
6.1 1
 
0.1%
6.2 3
0.3%
6.3 2
 
0.2%
6.4 2
 
0.2%
6.5 5
0.5%
6.7 6
0.6%
ValueCountFrequency (%)
8.1 2
 
0.2%
8 4
0.4%
7.9 3
 
0.3%
7.8 5
0.5%
7.7 5
0.5%
7.6 6
0.6%
7.5 8
0.8%
7.4 8
0.8%
7.3 9
0.9%
7.2 7
0.7%

OBS_FLAG_6
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing951
Missing (%)99.8%
Memory size7.6 KiB
2024-01-10T02:03:53.307590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb
2nd rowb
ValueCountFrequency (%)
b 2
100.0%
2024-01-10T02:03:53.511861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 2
100.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 2
100.0%

DATAFLOW_7
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
ESTAT:ILC_PW01(1.0)
27 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters513
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_PW01(1.0)
2nd rowESTAT:ILC_PW01(1.0)
3rd rowESTAT:ILC_PW01(1.0)
4th rowESTAT:ILC_PW01(1.0)
5th rowESTAT:ILC_PW01(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_PW01(1.0) 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:53.637630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:53.749860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_pw01(1.0 27
100.0%

Most occurring characters

ValueCountFrequency (%)
T 54
 
10.5%
0 54
 
10.5%
1 54
 
10.5%
E 27
 
5.3%
S 27
 
5.3%
A 27
 
5.3%
: 27
 
5.3%
I 27
 
5.3%
L 27
 
5.3%
C 27
 
5.3%
Other values (6) 162
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 270
52.6%
Decimal Number 108
 
21.1%
Other Punctuation 54
 
10.5%
Connector Punctuation 27
 
5.3%
Open Punctuation 27
 
5.3%
Close Punctuation 27
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 54
20.0%
E 27
10.0%
S 27
10.0%
A 27
10.0%
I 27
10.0%
L 27
10.0%
C 27
10.0%
P 27
10.0%
W 27
10.0%
Decimal Number
ValueCountFrequency (%)
0 54
50.0%
1 54
50.0%
Other Punctuation
ValueCountFrequency (%)
: 27
50.0%
. 27
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270
52.6%
Common 243
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 54
20.0%
E 27
10.0%
S 27
10.0%
A 27
10.0%
I 27
10.0%
L 27
10.0%
C 27
10.0%
P 27
10.0%
W 27
10.0%
Common
ValueCountFrequency (%)
0 54
22.2%
1 54
22.2%
: 27
11.1%
_ 27
11.1%
( 27
11.1%
. 27
11.1%
) 27
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 54
 
10.5%
0 54
 
10.5%
1 54
 
10.5%
E 27
 
5.3%
S 27
 
5.3%
A 27
 
5.3%
: 27
 
5.3%
I 27
 
5.3%
L 27
 
5.3%
C 27
 
5.3%
Other values (6) 162
31.6%

LAST UPDATE_7
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
15/12/23 23:00:00
27 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters459
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:53.842923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:53.936030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 27
50.0%
23:00:00 27
50.0%

Most occurring characters

ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 324
70.6%
Other Punctuation 108
 
23.5%
Space Separator 27
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 108
33.3%
2 81
25.0%
1 54
16.7%
3 54
16.7%
5 27
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 54
50.0%
: 54
50.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

freq_7
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
A
27 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:54.030221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:54.142511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 27
100.0%

Most occurring characters

ValueCountFrequency (%)
A 27
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 27
100.0%

ESTAT:ILC_PW01(1.0)_unit_7
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
RTG
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:54.218513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:54.328601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 27
100.0%

Most occurring characters

ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

isced11_7
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
TOTAL
27 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters135
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:54.407164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:54.516867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 27
100.0%

Most occurring characters

ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 135
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 135
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

indic_wb_7
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
LIVENVSAT
27 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters243
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLIVENVSAT
2nd rowLIVENVSAT
3rd rowLIVENVSAT
4th rowLIVENVSAT
5th rowLIVENVSAT

Common Values

ValueCountFrequency (%)
LIVENVSAT 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:54.611056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:54.705686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
livenvsat 27
100.0%

Most occurring characters

ValueCountFrequency (%)
V 54
22.2%
L 27
11.1%
I 27
11.1%
E 27
11.1%
N 27
11.1%
S 27
11.1%
A 27
11.1%
T 27
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 243
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
V 54
22.2%
L 27
11.1%
I 27
11.1%
E 27
11.1%
N 27
11.1%
S 27
11.1%
A 27
11.1%
T 27
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 243
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
V 54
22.2%
L 27
11.1%
I 27
11.1%
E 27
11.1%
N 27
11.1%
S 27
11.1%
A 27
11.1%
T 27
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
V 54
22.2%
L 27
11.1%
I 27
11.1%
E 27
11.1%
N 27
11.1%
S 27
11.1%
A 27
11.1%
T 27
11.1%

sex_7
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size2.0 KiB
True
 
27
(Missing)
926 
ValueCountFrequency (%)
True 27
 
2.8%
(Missing) 926
97.2%
2024-01-10T02:03:54.799724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age_7
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
Y_GE16
27 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters162
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_GE16
2nd rowY_GE16
3rd rowY_GE16
4th rowY_GE16
5th rowY_GE16

Common Values

ValueCountFrequency (%)
Y_GE16 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:54.894365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:54.988423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y_ge16 27
100.0%

Most occurring characters

ValueCountFrequency (%)
Y 27
16.7%
_ 27
16.7%
G 27
16.7%
E 27
16.7%
1 27
16.7%
6 27
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
50.0%
Decimal Number 54
33.3%
Connector Punctuation 27
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 27
33.3%
G 27
33.3%
E 27
33.3%
Decimal Number
ValueCountFrequency (%)
1 27
50.0%
6 27
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
50.0%
Common 81
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 27
33.3%
G 27
33.3%
E 27
33.3%
Common
ValueCountFrequency (%)
_ 27
33.3%
1 27
33.3%
6 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 27
16.7%
_ 27
16.7%
G 27
16.7%
E 27
16.7%
1 27
16.7%
6 27
16.7%

ESTAT:ILC_PW01(1.0)_VALUE_7
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)63.0%
Missing926
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean7.2
Minimum5.2
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:55.066978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile6
Q16.65
median7.5
Q37.75
95-th percentile8.14
Maximum8.4
Range3.2
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.79227035
Coefficient of variation (CV)0.11003755
Kurtosis-0.028015787
Mean7.2
Median Absolute Deviation (MAD)0.4
Skewness-0.78582801
Sum194.4
Variance0.62769231
MonotonicityNot monotonic
2024-01-10T02:03:55.176603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.6 4
 
0.4%
7.8 3
 
0.3%
6 2
 
0.2%
7.7 2
 
0.2%
8 2
 
0.2%
6.3 2
 
0.2%
7.2 2
 
0.2%
7.4 1
 
0.1%
7.1 1
 
0.1%
6.5 1
 
0.1%
Other values (7) 7
 
0.7%
(Missing) 926
97.2%
ValueCountFrequency (%)
5.2 1
0.1%
6 2
0.2%
6.2 1
0.1%
6.3 2
0.2%
6.5 1
0.1%
6.8 1
0.1%
6.9 1
0.1%
7.1 1
0.1%
7.2 2
0.2%
7.4 1
0.1%
ValueCountFrequency (%)
8.4 1
 
0.1%
8.2 1
 
0.1%
8 2
0.2%
7.8 3
0.3%
7.7 2
0.2%
7.6 4
0.4%
7.5 1
 
0.1%
7.4 1
 
0.1%
7.2 2
0.2%
7.1 1
 
0.1%

OBS_FLAG_7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing953
Missing (%)100.0%
Memory size7.6 KiB

DATAFLOW_8
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
ESTAT:ILC_PW01(1.0)
27 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters513
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_PW01(1.0)
2nd rowESTAT:ILC_PW01(1.0)
3rd rowESTAT:ILC_PW01(1.0)
4th rowESTAT:ILC_PW01(1.0)
5th rowESTAT:ILC_PW01(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_PW01(1.0) 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:55.286411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:55.396685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_pw01(1.0 27
100.0%

Most occurring characters

ValueCountFrequency (%)
T 54
 
10.5%
0 54
 
10.5%
1 54
 
10.5%
E 27
 
5.3%
S 27
 
5.3%
A 27
 
5.3%
: 27
 
5.3%
I 27
 
5.3%
L 27
 
5.3%
C 27
 
5.3%
Other values (6) 162
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 270
52.6%
Decimal Number 108
 
21.1%
Other Punctuation 54
 
10.5%
Connector Punctuation 27
 
5.3%
Open Punctuation 27
 
5.3%
Close Punctuation 27
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 54
20.0%
E 27
10.0%
S 27
10.0%
A 27
10.0%
I 27
10.0%
L 27
10.0%
C 27
10.0%
P 27
10.0%
W 27
10.0%
Decimal Number
ValueCountFrequency (%)
0 54
50.0%
1 54
50.0%
Other Punctuation
ValueCountFrequency (%)
: 27
50.0%
. 27
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270
52.6%
Common 243
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 54
20.0%
E 27
10.0%
S 27
10.0%
A 27
10.0%
I 27
10.0%
L 27
10.0%
C 27
10.0%
P 27
10.0%
W 27
10.0%
Common
ValueCountFrequency (%)
0 54
22.2%
1 54
22.2%
: 27
11.1%
_ 27
11.1%
( 27
11.1%
. 27
11.1%
) 27
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 54
 
10.5%
0 54
 
10.5%
1 54
 
10.5%
E 27
 
5.3%
S 27
 
5.3%
A 27
 
5.3%
: 27
 
5.3%
I 27
 
5.3%
L 27
 
5.3%
C 27
 
5.3%
Other values (6) 162
31.6%

LAST UPDATE_8
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
15/12/23 23:00:00
27 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters459
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:55.490769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:55.584944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 27
50.0%
23:00:00 27
50.0%

Most occurring characters

ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 324
70.6%
Other Punctuation 108
 
23.5%
Space Separator 27
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 108
33.3%
2 81
25.0%
1 54
16.7%
3 54
16.7%
5 27
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 54
50.0%
: 54
50.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 108
23.5%
2 81
17.6%
1 54
11.8%
/ 54
11.8%
3 54
11.8%
: 54
11.8%
5 27
 
5.9%
27
 
5.9%

freq_8
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
A
27 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:55.679374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:55.789183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 27
100.0%

Most occurring characters

ValueCountFrequency (%)
A 27
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 27
100.0%

ESTAT:ILC_PW01(1.0)_unit_8
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
RTG
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:55.868027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:55.977851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 27
100.0%

Most occurring characters

ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

isced11_8
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
TOTAL
27 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters135
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:56.056045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:56.165868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 27
100.0%

Most occurring characters

ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 135
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 135
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 54
40.0%
O 27
20.0%
A 27
20.0%
L 27
20.0%

indic_wb_8
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
MEANLIFE
27 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters216
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMEANLIFE
2nd rowMEANLIFE
3rd rowMEANLIFE
4th rowMEANLIFE
5th rowMEANLIFE

Common Values

ValueCountFrequency (%)
MEANLIFE 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:56.260465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:56.354780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
meanlife 27
100.0%

Most occurring characters

ValueCountFrequency (%)
E 54
25.0%
M 27
12.5%
A 27
12.5%
N 27
12.5%
L 27
12.5%
I 27
12.5%
F 27
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 216
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 54
25.0%
M 27
12.5%
A 27
12.5%
N 27
12.5%
L 27
12.5%
I 27
12.5%
F 27
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 216
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 54
25.0%
M 27
12.5%
A 27
12.5%
N 27
12.5%
L 27
12.5%
I 27
12.5%
F 27
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 54
25.0%
M 27
12.5%
A 27
12.5%
N 27
12.5%
L 27
12.5%
I 27
12.5%
F 27
12.5%

sex_8
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size2.0 KiB
True
 
27
(Missing)
926 
ValueCountFrequency (%)
True 27
 
2.8%
(Missing) 926
97.2%
2024-01-10T02:03:56.451089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age_8
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
Y_GE16
27 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters162
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_GE16
2nd rowY_GE16
3rd rowY_GE16
4th rowY_GE16
5th rowY_GE16

Common Values

ValueCountFrequency (%)
Y_GE16 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:03:56.544807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:56.637347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y_ge16 27
100.0%

Most occurring characters

ValueCountFrequency (%)
Y 27
16.7%
_ 27
16.7%
G 27
16.7%
E 27
16.7%
1 27
16.7%
6 27
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
50.0%
Decimal Number 54
33.3%
Connector Punctuation 27
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 27
33.3%
G 27
33.3%
E 27
33.3%
Decimal Number
ValueCountFrequency (%)
1 27
50.0%
6 27
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
50.0%
Common 81
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 27
33.3%
G 27
33.3%
E 27
33.3%
Common
ValueCountFrequency (%)
_ 27
33.3%
1 27
33.3%
6 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 27
16.7%
_ 27
16.7%
G 27
16.7%
E 27
16.7%
1 27
16.7%
6 27
16.7%

ESTAT:ILC_PW01(1.0)_VALUE_8
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)55.6%
Missing926
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean7.4814815
Minimum6.1
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:56.731207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile6.72
Q17.3
median7.5
Q37.8
95-th percentile8.07
Maximum8.2
Range2.1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.46163342
Coefficient of variation (CV)0.061703477
Kurtosis1.9828632
Mean7.4814815
Median Absolute Deviation (MAD)0.3
Skewness-1.0653139
Sum202
Variance0.21310541
MonotonicityNot monotonic
2024-01-10T02:03:56.825963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7.5 4
 
0.4%
7.6 3
 
0.3%
7.4 3
 
0.3%
7.9 2
 
0.2%
8 2
 
0.2%
7 2
 
0.2%
7.8 2
 
0.2%
7.3 2
 
0.2%
6.1 1
 
0.1%
7.2 1
 
0.1%
Other values (5) 5
 
0.5%
(Missing) 926
97.2%
ValueCountFrequency (%)
6.1 1
 
0.1%
6.6 1
 
0.1%
7 2
0.2%
7.1 1
 
0.1%
7.2 1
 
0.1%
7.3 2
0.2%
7.4 3
0.3%
7.5 4
0.4%
7.6 3
0.3%
7.7 1
 
0.1%
ValueCountFrequency (%)
8.2 1
 
0.1%
8.1 1
 
0.1%
8 2
0.2%
7.9 2
0.2%
7.8 2
0.2%
7.7 1
 
0.1%
7.6 3
0.3%
7.5 4
0.4%
7.4 3
0.3%
7.3 2
0.2%

OBS_FLAG_8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing953
Missing (%)100.0%
Memory size7.6 KiB

DATAFLOW_9
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
ESTAT:ILC_PW01(1.0)
54 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1026
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_PW01(1.0)
2nd rowESTAT:ILC_PW01(1.0)
3rd rowESTAT:ILC_PW01(1.0)
4th rowESTAT:ILC_PW01(1.0)
5th rowESTAT:ILC_PW01(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_PW01(1.0) 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:56.951266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:57.047934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_pw01(1.0 54
100.0%

Most occurring characters

ValueCountFrequency (%)
T 108
 
10.5%
0 108
 
10.5%
1 108
 
10.5%
E 54
 
5.3%
S 54
 
5.3%
A 54
 
5.3%
: 54
 
5.3%
I 54
 
5.3%
L 54
 
5.3%
C 54
 
5.3%
Other values (6) 324
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 540
52.6%
Decimal Number 216
 
21.1%
Other Punctuation 108
 
10.5%
Connector Punctuation 54
 
5.3%
Open Punctuation 54
 
5.3%
Close Punctuation 54
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 108
20.0%
E 54
10.0%
S 54
10.0%
A 54
10.0%
I 54
10.0%
L 54
10.0%
C 54
10.0%
P 54
10.0%
W 54
10.0%
Decimal Number
ValueCountFrequency (%)
0 108
50.0%
1 108
50.0%
Other Punctuation
ValueCountFrequency (%)
: 54
50.0%
. 54
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 540
52.6%
Common 486
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 108
20.0%
E 54
10.0%
S 54
10.0%
A 54
10.0%
I 54
10.0%
L 54
10.0%
C 54
10.0%
P 54
10.0%
W 54
10.0%
Common
ValueCountFrequency (%)
0 108
22.2%
1 108
22.2%
: 54
11.1%
_ 54
11.1%
( 54
11.1%
. 54
11.1%
) 54
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 108
 
10.5%
0 108
 
10.5%
1 108
 
10.5%
E 54
 
5.3%
S 54
 
5.3%
A 54
 
5.3%
: 54
 
5.3%
I 54
 
5.3%
L 54
 
5.3%
C 54
 
5.3%
Other values (6) 324
31.6%

LAST UPDATE_9
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
15/12/23 23:00:00
54 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters918
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:57.142500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:57.251828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 54
50.0%
23:00:00 54
50.0%

Most occurring characters

ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 648
70.6%
Other Punctuation 216
 
23.5%
Space Separator 54
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 216
33.3%
2 162
25.0%
1 108
16.7%
3 108
16.7%
5 54
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 108
50.0%
: 108
50.0%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 918
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

freq_9
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
A
54 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:57.344419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:57.464264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 54
100.0%

Most occurring characters

ValueCountFrequency (%)
A 54
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 54
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 54
100.0%

ESTAT:ILC_PW01(1.0)_unit_9
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
RTG
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters162
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:57.558332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:57.655399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 54
100.0%

Most occurring characters

ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

isced11_9
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
TOTAL
54 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters270
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:57.749082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:57.857011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 54
100.0%

Most occurring characters

ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 270
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

indic_wb_9
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
RELSAT
54 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters324
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRELSAT
2nd rowRELSAT
3rd rowRELSAT
4th rowRELSAT
5th rowRELSAT

Common Values

ValueCountFrequency (%)
RELSAT 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:57.935137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:58.047936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
relsat 54
100.0%

Most occurring characters

ValueCountFrequency (%)
R 54
16.7%
E 54
16.7%
L 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 324
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 54
16.7%
E 54
16.7%
L 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 324
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 54
16.7%
E 54
16.7%
L 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 54
16.7%
E 54
16.7%
L 54
16.7%
S 54
16.7%
A 54
16.7%
T 54
16.7%

sex_9
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size2.0 KiB
True
 
54
(Missing)
899 
ValueCountFrequency (%)
True 54
 
5.7%
(Missing) 899
94.3%
2024-01-10T02:03:58.141640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age_9
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
Y_GE16
54 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters324
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_GE16
2nd rowY_GE16
3rd rowY_GE16
4th rowY_GE16
5th rowY_GE16

Common Values

ValueCountFrequency (%)
Y_GE16 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:58.217643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:58.327625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y_ge16 54
100.0%

Most occurring characters

ValueCountFrequency (%)
Y 54
16.7%
_ 54
16.7%
G 54
16.7%
E 54
16.7%
1 54
16.7%
6 54
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
50.0%
Decimal Number 108
33.3%
Connector Punctuation 54
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 54
33.3%
G 54
33.3%
E 54
33.3%
Decimal Number
ValueCountFrequency (%)
1 54
50.0%
6 54
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
50.0%
Common 162
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 54
33.3%
G 54
33.3%
E 54
33.3%
Common
ValueCountFrequency (%)
_ 54
33.3%
1 54
33.3%
6 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 54
16.7%
_ 54
16.7%
G 54
16.7%
E 54
16.7%
1 54
16.7%
6 54
16.7%

ESTAT:ILC_PW01(1.0)_VALUE_9
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)31.5%
Missing899
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean7.9296296
Minimum5.7
Maximum8.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:03:58.406142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.7
5-th percentile7.065
Q17.725
median7.95
Q38.3
95-th percentile8.6
Maximum8.6
Range2.9
Interquartile range (IQR)0.575

Descriptive statistics

Standard deviation0.53363461
Coefficient of variation (CV)0.067296284
Kurtosis4.9311418
Mean7.9296296
Median Absolute Deviation (MAD)0.35
Skewness-1.6572423
Sum428.2
Variance0.2847659
MonotonicityNot monotonic
2024-01-10T02:03:58.515950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.9 7
 
0.7%
7.6 6
 
0.6%
7.8 6
 
0.6%
8.6 5
 
0.5%
8.2 5
 
0.5%
8.5 4
 
0.4%
8.3 4
 
0.4%
8 4
 
0.4%
8.1 3
 
0.3%
8.4 2
 
0.2%
Other values (7) 8
 
0.8%
(Missing) 899
94.3%
ValueCountFrequency (%)
5.7 1
 
0.1%
6.6 1
 
0.1%
7 1
 
0.1%
7.1 1
 
0.1%
7.3 2
 
0.2%
7.5 1
 
0.1%
7.6 6
0.6%
7.7 1
 
0.1%
7.8 6
0.6%
7.9 7
0.7%
ValueCountFrequency (%)
8.6 5
0.5%
8.5 4
0.4%
8.4 2
 
0.2%
8.3 4
0.4%
8.2 5
0.5%
8.1 3
0.3%
8 4
0.4%
7.9 7
0.7%
7.8 6
0.6%
7.7 1
 
0.1%

OBS_FLAG_9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing953
Missing (%)100.0%
Memory size7.6 KiB

DATAFLOW_10
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
ESTAT:ILC_PW01(1.0)
54 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1026
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:ILC_PW01(1.0)
2nd rowESTAT:ILC_PW01(1.0)
3rd rowESTAT:ILC_PW01(1.0)
4th rowESTAT:ILC_PW01(1.0)
5th rowESTAT:ILC_PW01(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:ILC_PW01(1.0) 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:58.626107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:58.735827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ilc_pw01(1.0 54
100.0%

Most occurring characters

ValueCountFrequency (%)
T 108
 
10.5%
0 108
 
10.5%
1 108
 
10.5%
E 54
 
5.3%
S 54
 
5.3%
A 54
 
5.3%
: 54
 
5.3%
I 54
 
5.3%
L 54
 
5.3%
C 54
 
5.3%
Other values (6) 324
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 540
52.6%
Decimal Number 216
 
21.1%
Other Punctuation 108
 
10.5%
Connector Punctuation 54
 
5.3%
Open Punctuation 54
 
5.3%
Close Punctuation 54
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 108
20.0%
E 54
10.0%
S 54
10.0%
A 54
10.0%
I 54
10.0%
L 54
10.0%
C 54
10.0%
P 54
10.0%
W 54
10.0%
Decimal Number
ValueCountFrequency (%)
0 108
50.0%
1 108
50.0%
Other Punctuation
ValueCountFrequency (%)
: 54
50.0%
. 54
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 540
52.6%
Common 486
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 108
20.0%
E 54
10.0%
S 54
10.0%
A 54
10.0%
I 54
10.0%
L 54
10.0%
C 54
10.0%
P 54
10.0%
W 54
10.0%
Common
ValueCountFrequency (%)
0 108
22.2%
1 108
22.2%
: 54
11.1%
_ 54
11.1%
( 54
11.1%
. 54
11.1%
) 54
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 108
 
10.5%
0 108
 
10.5%
1 108
 
10.5%
E 54
 
5.3%
S 54
 
5.3%
A 54
 
5.3%
: 54
 
5.3%
I 54
 
5.3%
L 54
 
5.3%
C 54
 
5.3%
Other values (6) 324
31.6%

LAST UPDATE_10
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
15/12/23 23:00:00
54 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters918
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/12/23 23:00:00
2nd row15/12/23 23:00:00
3rd row15/12/23 23:00:00
4th row15/12/23 23:00:00
5th row15/12/23 23:00:00

Common Values

ValueCountFrequency (%)
15/12/23 23:00:00 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:58.814789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:58.924477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15/12/23 54
50.0%
23:00:00 54
50.0%

Most occurring characters

ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 648
70.6%
Other Punctuation 216
 
23.5%
Space Separator 54
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 216
33.3%
2 162
25.0%
1 108
16.7%
3 108
16.7%
5 54
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 108
50.0%
: 108
50.0%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 918
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 216
23.5%
2 162
17.6%
1 108
11.8%
/ 108
11.8%
3 108
11.8%
: 108
11.8%
5 54
 
5.9%
54
 
5.9%

freq_10
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
A
54 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:59.018289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:59.128114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 54
100.0%

Most occurring characters

ValueCountFrequency (%)
A 54
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 54
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 54
100.0%

ESTAT:ILC_PW01(1.0)_unit_10
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
RTG
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters162
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:59.206544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:59.316905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 54
100.0%

Most occurring characters

ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

isced11_10
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
TOTAL
54 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters270
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:59.395250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:59.505062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 54
100.0%

Most occurring characters

ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 270
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 108
40.0%
O 54
20.0%
A 54
20.0%
L 54
20.0%

indic_wb_10
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
TIMESAT
54 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters378
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTIMESAT
2nd rowTIMESAT
3rd rowTIMESAT
4th rowTIMESAT
5th rowTIMESAT

Common Values

ValueCountFrequency (%)
TIMESAT 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:59.599289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:59.693905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
timesat 54
100.0%

Most occurring characters

ValueCountFrequency (%)
T 108
28.6%
I 54
14.3%
M 54
14.3%
E 54
14.3%
S 54
14.3%
A 54
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 378
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 108
28.6%
I 54
14.3%
M 54
14.3%
E 54
14.3%
S 54
14.3%
A 54
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 378
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 108
28.6%
I 54
14.3%
M 54
14.3%
E 54
14.3%
S 54
14.3%
A 54
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 108
28.6%
I 54
14.3%
M 54
14.3%
E 54
14.3%
S 54
14.3%
A 54
14.3%

sex_10
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size2.0 KiB
True
 
54
(Missing)
899 
ValueCountFrequency (%)
True 54
 
5.7%
(Missing) 899
94.3%
2024-01-10T02:03:59.787961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

age_10
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
Y_GE16
54 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters324
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_GE16
2nd rowY_GE16
3rd rowY_GE16
4th rowY_GE16
5th rowY_GE16

Common Values

ValueCountFrequency (%)
Y_GE16 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:03:59.866918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:03:59.976765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y_ge16 54
100.0%

Most occurring characters

ValueCountFrequency (%)
Y 54
16.7%
_ 54
16.7%
G 54
16.7%
E 54
16.7%
1 54
16.7%
6 54
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
50.0%
Decimal Number 108
33.3%
Connector Punctuation 54
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 54
33.3%
G 54
33.3%
E 54
33.3%
Decimal Number
ValueCountFrequency (%)
1 54
50.0%
6 54
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
50.0%
Common 162
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 54
33.3%
G 54
33.3%
E 54
33.3%
Common
ValueCountFrequency (%)
_ 54
33.3%
1 54
33.3%
6 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 54
16.7%
_ 54
16.7%
G 54
16.7%
E 54
16.7%
1 54
16.7%
6 54
16.7%

ESTAT:ILC_PW01(1.0)_VALUE_10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)33.3%
Missing899
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean6.8166667
Minimum5.5
Maximum7.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:04:00.058151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.5
5-th percentile5.96
Q16.6
median6.8
Q37.1
95-th percentile7.57
Maximum7.8
Range2.3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.4913401
Coefficient of variation (CV)0.072079232
Kurtosis0.59476157
Mean6.8166667
Median Absolute Deviation (MAD)0.25
Skewness-0.34705153
Sum368.1
Variance0.24141509
MonotonicityNot monotonic
2024-01-10T02:04:00.165346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
6.8 6
 
0.6%
6.6 6
 
0.6%
6.9 6
 
0.6%
6.7 5
 
0.5%
7 4
 
0.4%
7.2 3
 
0.3%
6.4 3
 
0.3%
7.3 3
 
0.3%
7.5 3
 
0.3%
6.3 2
 
0.2%
Other values (8) 13
 
1.4%
(Missing) 899
94.3%
ValueCountFrequency (%)
5.5 1
 
0.1%
5.7 2
 
0.2%
6.1 2
 
0.2%
6.3 2
 
0.2%
6.4 3
0.3%
6.5 2
 
0.2%
6.6 6
0.6%
6.7 5
0.5%
6.8 6
0.6%
6.9 6
0.6%
ValueCountFrequency (%)
7.8 2
 
0.2%
7.7 1
 
0.1%
7.5 3
0.3%
7.4 1
 
0.1%
7.3 3
0.3%
7.2 3
0.3%
7.1 2
 
0.2%
7 4
0.4%
6.9 6
0.6%
6.8 6
0.6%

OBS_FLAG_10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing953
Missing (%)100.0%
Memory size7.6 KiB

DATAFLOW_11
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing658
Missing (%)69.0%
Memory size7.6 KiB
ESTAT:TOUR_OCC_NINATS(1.0)
295 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters7670
Distinct characters17
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:TOUR_OCC_NINATS(1.0)
2nd rowESTAT:TOUR_OCC_NINATS(1.0)
3rd rowESTAT:TOUR_OCC_NINATS(1.0)
4th rowESTAT:TOUR_OCC_NINATS(1.0)
5th rowESTAT:TOUR_OCC_NINATS(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:TOUR_OCC_NINATS(1.0) 295
31.0%
(Missing) 658
69.0%

Length

2024-01-10T02:04:00.290725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:00.385081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:tour_occ_ninats(1.0 295
100.0%

Most occurring characters

ValueCountFrequency (%)
T 1180
15.4%
_ 590
 
7.7%
A 590
 
7.7%
O 590
 
7.7%
S 590
 
7.7%
C 590
 
7.7%
N 590
 
7.7%
( 295
 
3.8%
0 295
 
3.8%
. 295
 
3.8%
Other values (7) 2065
26.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5310
69.2%
Connector Punctuation 590
 
7.7%
Decimal Number 590
 
7.7%
Other Punctuation 590
 
7.7%
Open Punctuation 295
 
3.8%
Close Punctuation 295
 
3.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1180
22.2%
A 590
11.1%
O 590
11.1%
S 590
11.1%
C 590
11.1%
N 590
11.1%
E 295
 
5.6%
I 295
 
5.6%
R 295
 
5.6%
U 295
 
5.6%
Decimal Number
ValueCountFrequency (%)
0 295
50.0%
1 295
50.0%
Other Punctuation
ValueCountFrequency (%)
. 295
50.0%
: 295
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 590
100.0%
Open Punctuation
ValueCountFrequency (%)
( 295
100.0%
Close Punctuation
ValueCountFrequency (%)
) 295
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5310
69.2%
Common 2360
30.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1180
22.2%
A 590
11.1%
O 590
11.1%
S 590
11.1%
C 590
11.1%
N 590
11.1%
E 295
 
5.6%
I 295
 
5.6%
R 295
 
5.6%
U 295
 
5.6%
Common
ValueCountFrequency (%)
_ 590
25.0%
( 295
12.5%
0 295
12.5%
. 295
12.5%
1 295
12.5%
: 295
12.5%
) 295
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1180
15.4%
_ 590
 
7.7%
A 590
 
7.7%
O 590
 
7.7%
S 590
 
7.7%
C 590
 
7.7%
N 590
 
7.7%
( 295
 
3.8%
0 295
 
3.8%
. 295
 
3.8%
Other values (7) 2065
26.9%

LAST UPDATE_11
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing658
Missing (%)69.0%
Memory size7.6 KiB
30/11/23 23:00:00
295 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters5015
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30/11/23 23:00:00
2nd row30/11/23 23:00:00
3rd row30/11/23 23:00:00
4th row30/11/23 23:00:00
5th row30/11/23 23:00:00

Common Values

ValueCountFrequency (%)
30/11/23 23:00:00 295
31.0%
(Missing) 658
69.0%

Length

2024-01-10T02:04:00.479279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:00.588814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30/11/23 295
50.0%
23:00:00 295
50.0%

Most occurring characters

ValueCountFrequency (%)
0 1475
29.4%
3 885
17.6%
/ 590
 
11.8%
1 590
 
11.8%
2 590
 
11.8%
: 590
 
11.8%
295
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3540
70.6%
Other Punctuation 1180
 
23.5%
Space Separator 295
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1475
41.7%
3 885
25.0%
1 590
 
16.7%
2 590
 
16.7%
Other Punctuation
ValueCountFrequency (%)
/ 590
50.0%
: 590
50.0%
Space Separator
ValueCountFrequency (%)
295
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5015
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1475
29.4%
3 885
17.6%
/ 590
 
11.8%
1 590
 
11.8%
2 590
 
11.8%
: 590
 
11.8%
295
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1475
29.4%
3 885
17.6%
/ 590
 
11.8%
1 590
 
11.8%
2 590
 
11.8%
: 590
 
11.8%
295
 
5.9%

freq_11
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing658
Missing (%)69.0%
Memory size7.6 KiB
A
295 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters295
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 295
31.0%
(Missing) 658
69.0%

Length

2024-01-10T02:04:00.683352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:00.793169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 295
100.0%

Most occurring characters

ValueCountFrequency (%)
A 295
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 295
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 295
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 295
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 295
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 295
100.0%

c_resid
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing658
Missing (%)69.0%
Memory size7.6 KiB
TOTAL
295 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1475
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 295
31.0%
(Missing) 658
69.0%

Length

2024-01-10T02:04:00.872164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:00.982017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 295
100.0%

Most occurring characters

ValueCountFrequency (%)
T 590
40.0%
O 295
20.0%
A 295
20.0%
L 295
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1475
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 590
40.0%
O 295
20.0%
A 295
20.0%
L 295
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1475
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 590
40.0%
O 295
20.0%
A 295
20.0%
L 295
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 590
40.0%
O 295
20.0%
A 295
20.0%
L 295
20.0%

ESTAT:TOUR_OCC_NINATS(1.0)_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing658
Missing (%)69.0%
Memory size7.6 KiB
NR
295 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNR
2nd rowNR
3rd rowNR
4th rowNR
5th rowNR

Common Values

ValueCountFrequency (%)
NR 295
31.0%
(Missing) 658
69.0%

Length

2024-01-10T02:04:01.076101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:01.170288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nr 295
100.0%

Most occurring characters

ValueCountFrequency (%)
N 295
50.0%
R 295
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 590
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 295
50.0%
R 295
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 590
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 295
50.0%
R 295
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 295
50.0%
R 295
50.0%

hotelsize
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing658
Missing (%)69.0%
Memory size7.6 KiB
TOTAL
295 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1475
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 295
31.0%
(Missing) 658
69.0%

Length

2024-01-10T02:04:01.264635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:01.374759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 295
100.0%

Most occurring characters

ValueCountFrequency (%)
T 590
40.0%
O 295
20.0%
A 295
20.0%
L 295
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1475
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 590
40.0%
O 295
20.0%
A 295
20.0%
L 295
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1475
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 590
40.0%
O 295
20.0%
A 295
20.0%
L 295
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 590
40.0%
O 295
20.0%
A 295
20.0%
L 295
20.0%

ESTAT:TOUR_OCC_NINATS(1.0)_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct295
Distinct (%)100.0%
Missing658
Missing (%)69.0%
Infinite0
Infinite (%)0.0%
Mean57049184
Minimum840359
Maximum3.429956 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:04:01.484559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum840359
5-th percentile2782589.6
Q18987021
median21232963
Q351118864
95-th percentile2.7643455 × 108
Maximum3.429956 × 108
Range3.4215524 × 108
Interquartile range (IQR)42131842

Descriptive statistics

Standard deviation84602426
Coefficient of variation (CV)1.4829735
Kurtosis2.8915679
Mean57049184
Median Absolute Deviation (MAD)15058225
Skewness2.0332376
Sum1.6829509 × 1010
Variance7.1575705 × 1015
MonotonicityNot monotonic
2024-01-10T02:04:01.625963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3845346 1
 
0.1%
3374068 1
 
0.1%
3307837 1
 
0.1%
2954395 1
 
0.1%
2842382 1
 
0.1%
1596490 1
 
0.1%
1032999 1
 
0.1%
840359 1
 
0.1%
1654054 1
 
0.1%
1714113 1
 
0.1%
Other values (285) 285
29.9%
(Missing) 658
69.0%
ValueCountFrequency (%)
840359 1
0.1%
1032999 1
0.1%
1541786 1
0.1%
1569926 1
0.1%
1596490 1
0.1%
1654054 1
0.1%
1693749 1
0.1%
1698773 1
0.1%
1714113 1
0.1%
1738110 1
0.1%
ValueCountFrequency (%)
342995595 1
0.1%
340577818 1
0.1%
339980928 1
0.1%
331168945 1
0.1%
320366108 1
0.1%
308235728 1
0.1%
306848903 1
0.1%
297554891 1
0.1%
295260630 1
0.1%
288759266 1
0.1%

OBS_FLAG_11
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)13.3%
Missing938
Missing (%)98.4%
Memory size7.6 KiB
e
11 
b
4 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowe
2nd rowe
3rd rowb
4th rowe
5th rowe

Common Values

ValueCountFrequency (%)
e 11
 
1.2%
b 4
 
0.4%
(Missing) 938
98.4%

Length

2024-01-10T02:04:01.753910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:01.877414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
e 11
73.3%
b 4
 
26.7%

Most occurring characters

ValueCountFrequency (%)
e 11
73.3%
b 4
 
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11
73.3%
b 4
 
26.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 15
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11
73.3%
b 4
 
26.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11
73.3%
b 4
 
26.7%

DATAFLOW_12
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing170
Missing (%)17.8%
Memory size7.6 KiB
ESTAT:RAIL_TF_PASSMOV(1.0)
783 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters20358
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:RAIL_TF_PASSMOV(1.0)
2nd rowESTAT:RAIL_TF_PASSMOV(1.0)
3rd rowESTAT:RAIL_TF_PASSMOV(1.0)
4th rowESTAT:RAIL_TF_PASSMOV(1.0)
5th rowESTAT:RAIL_TF_PASSMOV(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:RAIL_TF_PASSMOV(1.0) 783
82.2%
(Missing) 170
 
17.8%

Length

2024-01-10T02:04:01.971611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:02.081133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:rail_tf_passmov(1.0 783
100.0%

Most occurring characters

ValueCountFrequency (%)
T 2349
 
11.5%
A 2349
 
11.5%
S 2349
 
11.5%
_ 1566
 
7.7%
E 783
 
3.8%
O 783
 
3.8%
0 783
 
3.8%
. 783
 
3.8%
1 783
 
3.8%
( 783
 
3.8%
Other values (9) 7047
34.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14094
69.2%
Connector Punctuation 1566
 
7.7%
Decimal Number 1566
 
7.7%
Other Punctuation 1566
 
7.7%
Open Punctuation 783
 
3.8%
Close Punctuation 783
 
3.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 2349
16.7%
A 2349
16.7%
S 2349
16.7%
E 783
 
5.6%
O 783
 
5.6%
V 783
 
5.6%
F 783
 
5.6%
M 783
 
5.6%
P 783
 
5.6%
L 783
 
5.6%
Other values (2) 1566
11.1%
Decimal Number
ValueCountFrequency (%)
0 783
50.0%
1 783
50.0%
Other Punctuation
ValueCountFrequency (%)
. 783
50.0%
: 783
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1566
100.0%
Open Punctuation
ValueCountFrequency (%)
( 783
100.0%
Close Punctuation
ValueCountFrequency (%)
) 783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14094
69.2%
Common 6264
30.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 2349
16.7%
A 2349
16.7%
S 2349
16.7%
E 783
 
5.6%
O 783
 
5.6%
V 783
 
5.6%
F 783
 
5.6%
M 783
 
5.6%
P 783
 
5.6%
L 783
 
5.6%
Other values (2) 1566
11.1%
Common
ValueCountFrequency (%)
_ 1566
25.0%
0 783
12.5%
. 783
12.5%
1 783
12.5%
( 783
12.5%
: 783
12.5%
) 783
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2349
 
11.5%
A 2349
 
11.5%
S 2349
 
11.5%
_ 1566
 
7.7%
E 783
 
3.8%
O 783
 
3.8%
0 783
 
3.8%
. 783
 
3.8%
1 783
 
3.8%
( 783
 
3.8%
Other values (9) 7047
34.6%

LAST UPDATE_12
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing170
Missing (%)17.8%
Memory size7.6 KiB
19/12/23 23:00:00
783 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters13311
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19/12/23 23:00:00
2nd row19/12/23 23:00:00
3rd row19/12/23 23:00:00
4th row19/12/23 23:00:00
5th row19/12/23 23:00:00

Common Values

ValueCountFrequency (%)
19/12/23 23:00:00 783
82.2%
(Missing) 170
 
17.8%

Length

2024-01-10T02:04:02.175331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:02.285077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
19/12/23 783
50.0%
23:00:00 783
50.0%

Most occurring characters

ValueCountFrequency (%)
0 3132
23.5%
2 2349
17.6%
1 1566
11.8%
/ 1566
11.8%
3 1566
11.8%
: 1566
11.8%
9 783
 
5.9%
783
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9396
70.6%
Other Punctuation 3132
 
23.5%
Space Separator 783
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3132
33.3%
2 2349
25.0%
1 1566
16.7%
3 1566
16.7%
9 783
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 1566
50.0%
: 1566
50.0%
Space Separator
ValueCountFrequency (%)
783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13311
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3132
23.5%
2 2349
17.6%
1 1566
11.8%
/ 1566
11.8%
3 1566
11.8%
: 1566
11.8%
9 783
 
5.9%
783
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3132
23.5%
2 2349
17.6%
1 1566
11.8%
/ 1566
11.8%
3 1566
11.8%
: 1566
11.8%
9 783
 
5.9%
783
 
5.9%

freq_12
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing170
Missing (%)17.8%
Memory size7.6 KiB
A
783 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters783
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 783
82.2%
(Missing) 170
 
17.8%

Length

2024-01-10T02:04:02.379695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:02.485796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 783
100.0%

Most occurring characters

ValueCountFrequency (%)
A 783
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 783
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 783
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 783
100.0%

ESTAT:RAIL_TF_PASSMOV(1.0)_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing170
Missing (%)17.8%
Memory size7.6 KiB
THS_TRKM
783 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters6264
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHS_TRKM
2nd rowTHS_TRKM
3rd rowTHS_TRKM
4th rowTHS_TRKM
5th rowTHS_TRKM

Common Values

ValueCountFrequency (%)
THS_TRKM 783
82.2%
(Missing) 170
 
17.8%

Length

2024-01-10T02:04:02.579988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:02.690231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ths_trkm 783
100.0%

Most occurring characters

ValueCountFrequency (%)
T 1566
25.0%
H 783
12.5%
S 783
12.5%
_ 783
12.5%
R 783
12.5%
K 783
12.5%
M 783
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5481
87.5%
Connector Punctuation 783
 
12.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1566
28.6%
H 783
14.3%
S 783
14.3%
R 783
14.3%
K 783
14.3%
M 783
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5481
87.5%
Common 783
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1566
28.6%
H 783
14.3%
S 783
14.3%
R 783
14.3%
K 783
14.3%
M 783
14.3%
Common
ValueCountFrequency (%)
_ 783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1566
25.0%
H 783
12.5%
S 783
12.5%
_ 783
12.5%
R 783
12.5%
K 783
12.5%
M 783
12.5%

vehicle
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing170
Missing (%)17.8%
Memory size7.6 KiB
TOTAL
783 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3915
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 783
82.2%
(Missing) 170
 
17.8%

Length

2024-01-10T02:04:02.784326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:02.878790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 783
100.0%

Most occurring characters

ValueCountFrequency (%)
T 1566
40.0%
O 783
20.0%
A 783
20.0%
L 783
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3915
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1566
40.0%
O 783
20.0%
A 783
20.0%
L 783
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3915
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1566
40.0%
O 783
20.0%
A 783
20.0%
L 783
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1566
40.0%
O 783
20.0%
A 783
20.0%
L 783
20.0%

ESTAT:RAIL_TF_PASSMOV(1.0)_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct777
Distinct (%)99.2%
Missing170
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean106326.38
Minimum2201
Maximum1090772
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:04:03.004236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2201
5-th percentile5303.8
Q115470.5
median61249
Q3123545.5
95-th percentile391778.1
Maximum1090772
Range1088571
Interquartile range (IQR)108075

Descriptive statistics

Standard deviation154716.76
Coefficient of variation (CV)1.4551117
Kurtosis12.997458
Mean106326.38
Median Absolute Deviation (MAD)48004
Skewness3.2276455
Sum83253553
Variance2.3937275 × 1010
MonotonicityNot monotonic
2024-01-10T02:04:03.148259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62068 2
 
0.2%
22150 2
 
0.2%
27105 2
 
0.2%
41610 2
 
0.2%
61200 2
 
0.2%
11939 2
 
0.2%
5480 1
 
0.1%
5426 1
 
0.1%
41350 1
 
0.1%
41430 1
 
0.1%
Other values (767) 767
80.5%
(Missing) 170
 
17.8%
ValueCountFrequency (%)
2201 1
0.1%
2714 1
0.1%
2822 1
0.1%
2838 1
0.1%
2858 1
0.1%
2884 1
0.1%
2893 1
0.1%
2914 1
0.1%
2990 1
0.1%
3036 1
0.1%
ValueCountFrequency (%)
1090772 1
0.1%
1080000 1
0.1%
1079700 1
0.1%
1079000 1
0.1%
848000 1
0.1%
811100 1
0.1%
809000 1
0.1%
808000 1
0.1%
806000 1
0.1%
799000 1
0.1%

OBS_FLAG_12
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)13.3%
Missing938
Missing (%)98.4%
Memory size7.6 KiB
d
12 
b
3 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd
2nd rowd
3rd rowd
4th rowd
5th rowd

Common Values

ValueCountFrequency (%)
d 12
 
1.3%
b 3
 
0.3%
(Missing) 938
98.4%

Length

2024-01-10T02:04:03.271074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:03.380992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
d 12
80.0%
b 3
 
20.0%

Most occurring characters

ValueCountFrequency (%)
d 12
80.0%
b 3
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 12
80.0%
b 3
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 12
80.0%
b 3
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 12
80.0%
b 3
 
20.0%

DATAFLOW_13
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing481
Missing (%)50.5%
Memory size7.6 KiB
ESTAT:RAIL_PA_TOTAL(1.0)
472 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters11328
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:RAIL_PA_TOTAL(1.0)
2nd rowESTAT:RAIL_PA_TOTAL(1.0)
3rd rowESTAT:RAIL_PA_TOTAL(1.0)
4th rowESTAT:RAIL_PA_TOTAL(1.0)
5th rowESTAT:RAIL_PA_TOTAL(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:RAIL_PA_TOTAL(1.0) 472
49.5%
(Missing) 481
50.5%

Length

2024-01-10T02:04:03.490802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:03.584896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:rail_pa_total(1.0 472
100.0%

Most occurring characters

ValueCountFrequency (%)
T 1888
16.7%
A 1888
16.7%
L 944
 
8.3%
_ 944
 
8.3%
E 472
 
4.2%
S 472
 
4.2%
: 472
 
4.2%
R 472
 
4.2%
I 472
 
4.2%
P 472
 
4.2%
Other values (6) 2832
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7552
66.7%
Connector Punctuation 944
 
8.3%
Other Punctuation 944
 
8.3%
Decimal Number 944
 
8.3%
Open Punctuation 472
 
4.2%
Close Punctuation 472
 
4.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1888
25.0%
A 1888
25.0%
L 944
12.5%
E 472
 
6.2%
S 472
 
6.2%
R 472
 
6.2%
I 472
 
6.2%
P 472
 
6.2%
O 472
 
6.2%
Other Punctuation
ValueCountFrequency (%)
: 472
50.0%
. 472
50.0%
Decimal Number
ValueCountFrequency (%)
1 472
50.0%
0 472
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 944
100.0%
Open Punctuation
ValueCountFrequency (%)
( 472
100.0%
Close Punctuation
ValueCountFrequency (%)
) 472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7552
66.7%
Common 3776
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1888
25.0%
A 1888
25.0%
L 944
12.5%
E 472
 
6.2%
S 472
 
6.2%
R 472
 
6.2%
I 472
 
6.2%
P 472
 
6.2%
O 472
 
6.2%
Common
ValueCountFrequency (%)
_ 944
25.0%
: 472
12.5%
( 472
12.5%
1 472
12.5%
. 472
12.5%
0 472
12.5%
) 472
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1888
16.7%
A 1888
16.7%
L 944
 
8.3%
_ 944
 
8.3%
E 472
 
4.2%
S 472
 
4.2%
: 472
 
4.2%
R 472
 
4.2%
I 472
 
4.2%
P 472
 
4.2%
Other values (6) 2832
25.0%

LAST UPDATE_13
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing481
Missing (%)50.5%
Memory size7.6 KiB
29/11/23 23:00:00
472 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters8024
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29/11/23 23:00:00
2nd row29/11/23 23:00:00
3rd row29/11/23 23:00:00
4th row29/11/23 23:00:00
5th row29/11/23 23:00:00

Common Values

ValueCountFrequency (%)
29/11/23 23:00:00 472
49.5%
(Missing) 481
50.5%

Length

2024-01-10T02:04:03.679387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:03.773582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
29/11/23 472
50.0%
23:00:00 472
50.0%

Most occurring characters

ValueCountFrequency (%)
0 1888
23.5%
2 1416
17.6%
/ 944
11.8%
1 944
11.8%
3 944
11.8%
: 944
11.8%
9 472
 
5.9%
472
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5664
70.6%
Other Punctuation 1888
 
23.5%
Space Separator 472
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1888
33.3%
2 1416
25.0%
1 944
16.7%
3 944
16.7%
9 472
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 944
50.0%
: 944
50.0%
Space Separator
ValueCountFrequency (%)
472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8024
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1888
23.5%
2 1416
17.6%
/ 944
11.8%
1 944
11.8%
3 944
11.8%
: 944
11.8%
9 472
 
5.9%
472
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1888
23.5%
2 1416
17.6%
/ 944
11.8%
1 944
11.8%
3 944
11.8%
: 944
11.8%
9 472
 
5.9%
472
 
5.9%

freq_13
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing481
Missing (%)50.5%
Memory size7.6 KiB
A
472 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters472
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 472
49.5%
(Missing) 481
50.5%

Length

2024-01-10T02:04:03.854858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:03.962043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 472
100.0%

Most occurring characters

ValueCountFrequency (%)
A 472
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 472
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 472
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 472
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 472
100.0%

ESTAT:RAIL_PA_TOTAL(1.0)_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing481
Missing (%)50.5%
Memory size7.6 KiB
MIO_PKM
472 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3304
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIO_PKM
2nd rowMIO_PKM
3rd rowMIO_PKM
4th rowMIO_PKM
5th rowMIO_PKM

Common Values

ValueCountFrequency (%)
MIO_PKM 472
49.5%
(Missing) 481
50.5%

Length

2024-01-10T02:04:04.043071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:04.152387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mio_pkm 472
100.0%

Most occurring characters

ValueCountFrequency (%)
M 944
28.6%
I 472
14.3%
O 472
14.3%
_ 472
14.3%
P 472
14.3%
K 472
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2832
85.7%
Connector Punctuation 472
 
14.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 944
33.3%
I 472
16.7%
O 472
16.7%
P 472
16.7%
K 472
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2832
85.7%
Common 472
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 944
33.3%
I 472
16.7%
O 472
16.7%
P 472
16.7%
K 472
16.7%
Common
ValueCountFrequency (%)
_ 472
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 944
28.6%
I 472
14.3%
O 472
14.3%
_ 472
14.3%
P 472
14.3%
K 472
14.3%

ESTAT:RAIL_PA_TOTAL(1.0)_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct414
Distinct (%)96.3%
Missing523
Missing (%)54.9%
Infinite0
Infinite (%)0.0%
Mean13940.24
Minimum193
Maximum102814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:04:04.247754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum193
5-th percentile274
Q1962.25
median4147.5
Q311688.75
95-th percentile82652.95
Maximum102814
Range102621
Interquartile range (IQR)10726.5

Descriptive statistics

Standard deviation24157.281
Coefficient of variation (CV)1.7329172
Kurtosis4.548878
Mean13940.24
Median Absolute Deviation (MAD)3612.5
Skewness2.3758848
Sum5994303
Variance5.8357423 × 108
MonotonicityNot monotonic
2024-01-10T02:04:04.400958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280 3
 
0.3%
3876 2
 
0.2%
382 2
 
0.2%
268 2
 
0.2%
4271 2
 
0.2%
9403 2
 
0.2%
278 2
 
0.2%
941 2
 
0.2%
724 2
 
0.2%
3957 2
 
0.2%
Other values (404) 409
42.9%
(Missing) 523
54.9%
ValueCountFrequency (%)
193 1
0.1%
223 1
0.1%
231 1
0.1%
235 1
0.1%
237 1
0.1%
243 1
0.1%
244 1
0.1%
246 1
0.1%
247 1
0.1%
248 1
0.1%
ValueCountFrequency (%)
102814 1
0.1%
100252 1
0.1%
98161 1
0.1%
96540 1
0.1%
95529 1
0.1%
95465 1
0.1%
95024 1
0.1%
93918 1
0.1%
92313 1
0.1%
91832 1
0.1%

OBS_FLAG_13
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)4.7%
Missing910
Missing (%)95.5%
Memory size7.6 KiB
c
41 
p
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowc
2nd rowc
3rd rowc
4th rowc
5th rowc

Common Values

ValueCountFrequency (%)
c 41
 
4.3%
p 2
 
0.2%
(Missing) 910
95.5%

Length

2024-01-10T02:04:04.510773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:04.636543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 41
95.3%
p 2
 
4.7%

Most occurring characters

ValueCountFrequency (%)
c 41
95.3%
p 2
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 41
95.3%
p 2
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 43
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 41
95.3%
p 2
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 41
95.3%
p 2
 
4.7%

DATAFLOW_14
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing481
Missing (%)50.5%
Memory size7.6 KiB
ESTAT:RAIL_PA_TOTAL(1.0)
472 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters11328
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:RAIL_PA_TOTAL(1.0)
2nd rowESTAT:RAIL_PA_TOTAL(1.0)
3rd rowESTAT:RAIL_PA_TOTAL(1.0)
4th rowESTAT:RAIL_PA_TOTAL(1.0)
5th rowESTAT:RAIL_PA_TOTAL(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:RAIL_PA_TOTAL(1.0) 472
49.5%
(Missing) 481
50.5%

Length

2024-01-10T02:04:04.730427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:04.825133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:rail_pa_total(1.0 472
100.0%

Most occurring characters

ValueCountFrequency (%)
T 1888
16.7%
A 1888
16.7%
L 944
 
8.3%
_ 944
 
8.3%
E 472
 
4.2%
S 472
 
4.2%
: 472
 
4.2%
R 472
 
4.2%
I 472
 
4.2%
P 472
 
4.2%
Other values (6) 2832
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7552
66.7%
Connector Punctuation 944
 
8.3%
Other Punctuation 944
 
8.3%
Decimal Number 944
 
8.3%
Open Punctuation 472
 
4.2%
Close Punctuation 472
 
4.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1888
25.0%
A 1888
25.0%
L 944
12.5%
E 472
 
6.2%
S 472
 
6.2%
R 472
 
6.2%
I 472
 
6.2%
P 472
 
6.2%
O 472
 
6.2%
Other Punctuation
ValueCountFrequency (%)
: 472
50.0%
. 472
50.0%
Decimal Number
ValueCountFrequency (%)
1 472
50.0%
0 472
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 944
100.0%
Open Punctuation
ValueCountFrequency (%)
( 472
100.0%
Close Punctuation
ValueCountFrequency (%)
) 472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7552
66.7%
Common 3776
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1888
25.0%
A 1888
25.0%
L 944
12.5%
E 472
 
6.2%
S 472
 
6.2%
R 472
 
6.2%
I 472
 
6.2%
P 472
 
6.2%
O 472
 
6.2%
Common
ValueCountFrequency (%)
_ 944
25.0%
: 472
12.5%
( 472
12.5%
1 472
12.5%
. 472
12.5%
0 472
12.5%
) 472
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1888
16.7%
A 1888
16.7%
L 944
 
8.3%
_ 944
 
8.3%
E 472
 
4.2%
S 472
 
4.2%
: 472
 
4.2%
R 472
 
4.2%
I 472
 
4.2%
P 472
 
4.2%
Other values (6) 2832
25.0%

LAST UPDATE_14
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing481
Missing (%)50.5%
Memory size7.6 KiB
29/11/23 23:00:00
472 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters8024
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29/11/23 23:00:00
2nd row29/11/23 23:00:00
3rd row29/11/23 23:00:00
4th row29/11/23 23:00:00
5th row29/11/23 23:00:00

Common Values

ValueCountFrequency (%)
29/11/23 23:00:00 472
49.5%
(Missing) 481
50.5%

Length

2024-01-10T02:04:04.919220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:05.013259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
29/11/23 472
50.0%
23:00:00 472
50.0%

Most occurring characters

ValueCountFrequency (%)
0 1888
23.5%
2 1416
17.6%
/ 944
11.8%
1 944
11.8%
3 944
11.8%
: 944
11.8%
9 472
 
5.9%
472
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5664
70.6%
Other Punctuation 1888
 
23.5%
Space Separator 472
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1888
33.3%
2 1416
25.0%
1 944
16.7%
3 944
16.7%
9 472
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 944
50.0%
: 944
50.0%
Space Separator
ValueCountFrequency (%)
472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8024
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1888
23.5%
2 1416
17.6%
/ 944
11.8%
1 944
11.8%
3 944
11.8%
: 944
11.8%
9 472
 
5.9%
472
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1888
23.5%
2 1416
17.6%
/ 944
11.8%
1 944
11.8%
3 944
11.8%
: 944
11.8%
9 472
 
5.9%
472
 
5.9%

freq_14
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing481
Missing (%)50.5%
Memory size7.6 KiB
A
472 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters472
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 472
49.5%
(Missing) 481
50.5%

Length

2024-01-10T02:04:05.107438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:05.201497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 472
100.0%

Most occurring characters

ValueCountFrequency (%)
A 472
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 472
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 472
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 472
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 472
100.0%

ESTAT:RAIL_PA_TOTAL(1.0)_unit_14
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing481
Missing (%)50.5%
Memory size7.6 KiB
THS_PAS
472 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3304
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHS_PAS
2nd rowTHS_PAS
3rd rowTHS_PAS
4th rowTHS_PAS
5th rowTHS_PAS

Common Values

ValueCountFrequency (%)
THS_PAS 472
49.5%
(Missing) 481
50.5%

Length

2024-01-10T02:04:05.295688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:05.390171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ths_pas 472
100.0%

Most occurring characters

ValueCountFrequency (%)
S 944
28.6%
T 472
14.3%
H 472
14.3%
_ 472
14.3%
P 472
14.3%
A 472
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2832
85.7%
Connector Punctuation 472
 
14.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 944
33.3%
T 472
16.7%
H 472
16.7%
P 472
16.7%
A 472
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2832
85.7%
Common 472
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 944
33.3%
T 472
16.7%
H 472
16.7%
P 472
16.7%
A 472
16.7%
Common
ValueCountFrequency (%)
_ 472
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 944
28.6%
T 472
14.3%
H 472
14.3%
_ 472
14.3%
P 472
14.3%
A 472
14.3%

ESTAT:RAIL_PA_TOTAL(1.0)_VALUE_14
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct430
Distinct (%)99.8%
Missing522
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean286126.39
Minimum3238
Maximum2938023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:04:05.484375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3238
5-th percentile5216.5
Q120345.5
median77265
Q3233303
95-th percentile1240802
Maximum2938023
Range2934785
Interquartile range (IQR)212957.5

Descriptive statistics

Standard deviation536890.47
Coefficient of variation (CV)1.8764102
Kurtosis10.461147
Mean286126.39
Median Absolute Deviation (MAD)71114
Skewness3.1907096
Sum1.2332047 × 108
Variance2.8825138 × 1011
MonotonicityNot monotonic
2024-01-10T02:04:05.625851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21329 2
 
0.2%
20804 1
 
0.1%
21504 1
 
0.1%
26702 1
 
0.1%
27380 1
 
0.1%
27387 1
 
0.1%
25915 1
 
0.1%
22038 1
 
0.1%
16595 1
 
0.1%
14527 1
 
0.1%
Other values (420) 420
44.1%
(Missing) 522
54.8%
ValueCountFrequency (%)
3238 1
0.1%
3790 1
0.1%
3795 1
0.1%
3819 1
0.1%
3916 1
0.1%
3948 1
0.1%
4126 1
0.1%
4127 1
0.1%
4176 1
0.1%
4199 1
0.1%
ValueCountFrequency (%)
2938023 1
0.1%
2880558 1
0.1%
2831443 1
0.1%
2813782 1
0.1%
2693080 1
0.1%
2684908 1
0.1%
2612764 1
0.1%
2564498 1
0.1%
2530284 1
0.1%
2505856 1
0.1%

OBS_FLAG_14
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)4.7%
Missing910
Missing (%)95.5%
Memory size7.6 KiB
c
41 
p
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowc
2nd rowc
3rd rowc
4th rowc
5th rowc

Common Values

ValueCountFrequency (%)
c 41
 
4.3%
p 2
 
0.2%
(Missing) 910
95.5%

Length

2024-01-10T02:04:05.753616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:05.877255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 41
95.3%
p 2
 
4.7%

Most occurring characters

ValueCountFrequency (%)
c 41
95.3%
p 2
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 41
95.3%
p 2
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 43
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 41
95.3%
p 2
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 41
95.3%
p 2
 
4.7%

DATAFLOW_15
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing627
Missing (%)65.8%
Memory size7.6 KiB
ESTAT:RAIL_AC_CATNMBR(1.0)
326 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters8476
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:RAIL_AC_CATNMBR(1.0)
2nd rowESTAT:RAIL_AC_CATNMBR(1.0)
3rd rowESTAT:RAIL_AC_CATNMBR(1.0)
4th rowESTAT:RAIL_AC_CATNMBR(1.0)
5th rowESTAT:RAIL_AC_CATNMBR(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:RAIL_AC_CATNMBR(1.0) 326
34.2%
(Missing) 627
65.8%

Length

2024-01-10T02:04:05.971443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:06.080990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:rail_ac_catnmbr(1.0 326
100.0%

Most occurring characters

ValueCountFrequency (%)
A 1304
15.4%
T 978
 
11.5%
C 652
 
7.7%
R 652
 
7.7%
_ 652
 
7.7%
M 326
 
3.8%
0 326
 
3.8%
. 326
 
3.8%
1 326
 
3.8%
( 326
 
3.8%
Other values (8) 2608
30.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5868
69.2%
Connector Punctuation 652
 
7.7%
Decimal Number 652
 
7.7%
Other Punctuation 652
 
7.7%
Open Punctuation 326
 
3.8%
Close Punctuation 326
 
3.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1304
22.2%
T 978
16.7%
C 652
11.1%
R 652
11.1%
M 326
 
5.6%
B 326
 
5.6%
E 326
 
5.6%
N 326
 
5.6%
S 326
 
5.6%
L 326
 
5.6%
Decimal Number
ValueCountFrequency (%)
0 326
50.0%
1 326
50.0%
Other Punctuation
ValueCountFrequency (%)
. 326
50.0%
: 326
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 652
100.0%
Open Punctuation
ValueCountFrequency (%)
( 326
100.0%
Close Punctuation
ValueCountFrequency (%)
) 326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5868
69.2%
Common 2608
30.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1304
22.2%
T 978
16.7%
C 652
11.1%
R 652
11.1%
M 326
 
5.6%
B 326
 
5.6%
E 326
 
5.6%
N 326
 
5.6%
S 326
 
5.6%
L 326
 
5.6%
Common
ValueCountFrequency (%)
_ 652
25.0%
0 326
12.5%
. 326
12.5%
1 326
12.5%
( 326
12.5%
: 326
12.5%
) 326
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1304
15.4%
T 978
 
11.5%
C 652
 
7.7%
R 652
 
7.7%
_ 652
 
7.7%
M 326
 
3.8%
0 326
 
3.8%
. 326
 
3.8%
1 326
 
3.8%
( 326
 
3.8%
Other values (8) 2608
30.8%

LAST UPDATE_15
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing627
Missing (%)65.8%
Memory size7.6 KiB
18/03/19 23:00:00
326 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters5542
Distinct characters9
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18/03/19 23:00:00
2nd row18/03/19 23:00:00
3rd row18/03/19 23:00:00
4th row18/03/19 23:00:00
5th row18/03/19 23:00:00

Common Values

ValueCountFrequency (%)
18/03/19 23:00:00 326
34.2%
(Missing) 627
65.8%

Length

2024-01-10T02:04:06.175179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:06.285133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
18/03/19 326
50.0%
23:00:00 326
50.0%

Most occurring characters

ValueCountFrequency (%)
0 1630
29.4%
1 652
 
11.8%
/ 652
 
11.8%
3 652
 
11.8%
: 652
 
11.8%
8 326
 
5.9%
9 326
 
5.9%
326
 
5.9%
2 326
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3912
70.6%
Other Punctuation 1304
 
23.5%
Space Separator 326
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1630
41.7%
1 652
 
16.7%
3 652
 
16.7%
8 326
 
8.3%
9 326
 
8.3%
2 326
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 652
50.0%
: 652
50.0%
Space Separator
ValueCountFrequency (%)
326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5542
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1630
29.4%
1 652
 
11.8%
/ 652
 
11.8%
3 652
 
11.8%
: 652
 
11.8%
8 326
 
5.9%
9 326
 
5.9%
326
 
5.9%
2 326
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1630
29.4%
1 652
 
11.8%
/ 652
 
11.8%
3 652
 
11.8%
: 652
 
11.8%
8 326
 
5.9%
9 326
 
5.9%
326
 
5.9%
2 326
 
5.9%

freq_15
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing627
Missing (%)65.8%
Memory size7.6 KiB
A
326 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters326
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 326
34.2%
(Missing) 627
65.8%

Length

2024-01-10T02:04:06.379571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:06.489408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 326
100.0%

Most occurring characters

ValueCountFrequency (%)
A 326
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 326
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 326
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 326
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 326
100.0%

ESTAT:RAIL_AC_CATNMBR(1.0)_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing627
Missing (%)65.8%
Memory size7.6 KiB
NR
326 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters652
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNR
2nd rowNR
3rd rowNR
4th rowNR
5th rowNR

Common Values

ValueCountFrequency (%)
NR 326
34.2%
(Missing) 627
65.8%

Length

2024-01-10T02:04:06.583282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:06.693078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nr 326
100.0%

Most occurring characters

ValueCountFrequency (%)
N 326
50.0%
R 326
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 652
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 326
50.0%
R 326
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 652
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 326
50.0%
R 326
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 326
50.0%
R 326
50.0%

accident
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing627
Missing (%)65.8%
Memory size7.6 KiB
TOTAL
326 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1630
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 326
34.2%
(Missing) 627
65.8%

Length

2024-01-10T02:04:06.787127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:06.897197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 326
100.0%

Most occurring characters

ValueCountFrequency (%)
T 652
40.0%
O 326
20.0%
A 326
20.0%
L 326
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1630
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 652
40.0%
O 326
20.0%
A 326
20.0%
L 326
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1630
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 652
40.0%
O 326
20.0%
A 326
20.0%
L 326
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 652
40.0%
O 326
20.0%
A 326
20.0%
L 326
20.0%

ESTAT:RAIL_AC_CATNMBR(1.0)_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct160
Distinct (%)53.3%
Missing653
Missing (%)68.5%
Infinite0
Infinite (%)0.0%
Mean139.66
Minimum0
Maximum2198
Zeros9
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:04:07.007023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.95
Q128
median62.5
Q3132.25
95-th percentile626.65
Maximum2198
Range2198
Interquartile range (IQR)104.25

Descriptive statistics

Standard deviation251.41621
Coefficient of variation (CV)1.800202
Kurtosis25.085715
Mean139.66
Median Absolute Deviation (MAD)40.5
Skewness4.4313355
Sum41898
Variance63210.111
MonotonicityNot monotonic
2024-01-10T02:04:07.856200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
0.9%
48 7
 
0.7%
42 6
 
0.6%
33 5
 
0.5%
1 5
 
0.5%
27 5
 
0.5%
39 4
 
0.4%
28 4
 
0.4%
40 4
 
0.4%
94 4
 
0.4%
Other values (150) 247
 
25.9%
(Missing) 653
68.5%
ValueCountFrequency (%)
0 9
0.9%
1 5
0.5%
2 1
 
0.1%
3 2
 
0.2%
4 3
 
0.3%
5 4
0.4%
7 1
 
0.1%
11 3
 
0.3%
12 3
 
0.3%
13 3
 
0.3%
ValueCountFrequency (%)
2198 1
0.1%
1863 1
0.1%
1172 1
0.1%
1150 1
0.1%
1111 1
0.1%
976 1
0.1%
964 1
0.1%
961 1
0.1%
905 1
0.1%
883 1
0.1%

OBS_FLAG_15
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.8%
Missing927
Missing (%)97.3%
Memory size7.6 KiB
z
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowz
2nd rowz
3rd rowz
4th rowz
5th rowz

Common Values

ValueCountFrequency (%)
z 26
 
2.7%
(Missing) 927
97.3%

Length

2024-01-10T02:04:07.981654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:08.075516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
z 26
100.0%

Most occurring characters

ValueCountFrequency (%)
z 26
100.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
z 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
z 26
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
z 26
100.0%

DATAFLOW_16
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing653
Missing (%)68.5%
Memory size7.6 KiB
ESTAT:TTR00003(1.0)
300 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters5700
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTAT:TTR00003(1.0)
2nd rowESTAT:TTR00003(1.0)
3rd rowESTAT:TTR00003(1.0)
4th rowESTAT:TTR00003(1.0)
5th rowESTAT:TTR00003(1.0)

Common Values

ValueCountFrequency (%)
ESTAT:TTR00003(1.0) 300
31.5%
(Missing) 653
68.5%

Length

2024-01-10T02:04:08.169697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:08.279541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
estat:ttr00003(1.0 300
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1500
26.3%
T 1200
21.1%
E 300
 
5.3%
S 300
 
5.3%
A 300
 
5.3%
: 300
 
5.3%
R 300
 
5.3%
3 300
 
5.3%
( 300
 
5.3%
1 300
 
5.3%
Other values (2) 600
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2400
42.1%
Decimal Number 2100
36.8%
Other Punctuation 600
 
10.5%
Open Punctuation 300
 
5.3%
Close Punctuation 300
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1200
50.0%
E 300
 
12.5%
S 300
 
12.5%
A 300
 
12.5%
R 300
 
12.5%
Decimal Number
ValueCountFrequency (%)
0 1500
71.4%
3 300
 
14.3%
1 300
 
14.3%
Other Punctuation
ValueCountFrequency (%)
: 300
50.0%
. 300
50.0%
Open Punctuation
ValueCountFrequency (%)
( 300
100.0%
Close Punctuation
ValueCountFrequency (%)
) 300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3300
57.9%
Latin 2400
42.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1500
45.5%
: 300
 
9.1%
3 300
 
9.1%
( 300
 
9.1%
1 300
 
9.1%
. 300
 
9.1%
) 300
 
9.1%
Latin
ValueCountFrequency (%)
T 1200
50.0%
E 300
 
12.5%
S 300
 
12.5%
A 300
 
12.5%
R 300
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1500
26.3%
T 1200
21.1%
E 300
 
5.3%
S 300
 
5.3%
A 300
 
5.3%
: 300
 
5.3%
R 300
 
5.3%
3 300
 
5.3%
( 300
 
5.3%
1 300
 
5.3%
Other values (2) 600
 
10.5%

LAST UPDATE_16
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing653
Missing (%)68.5%
Memory size7.6 KiB
19/12/23 23:00:00
300 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters5100
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19/12/23 23:00:00
2nd row19/12/23 23:00:00
3rd row19/12/23 23:00:00
4th row19/12/23 23:00:00
5th row19/12/23 23:00:00

Common Values

ValueCountFrequency (%)
19/12/23 23:00:00 300
31.5%
(Missing) 653
68.5%

Length

2024-01-10T02:04:08.374121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:08.483735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
19/12/23 300
50.0%
23:00:00 300
50.0%

Most occurring characters

ValueCountFrequency (%)
0 1200
23.5%
2 900
17.6%
1 600
11.8%
/ 600
11.8%
3 600
11.8%
: 600
11.8%
9 300
 
5.9%
300
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3600
70.6%
Other Punctuation 1200
 
23.5%
Space Separator 300
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1200
33.3%
2 900
25.0%
1 600
16.7%
3 600
16.7%
9 300
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 600
50.0%
: 600
50.0%
Space Separator
ValueCountFrequency (%)
300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1200
23.5%
2 900
17.6%
1 600
11.8%
/ 600
11.8%
3 600
11.8%
: 600
11.8%
9 300
 
5.9%
300
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1200
23.5%
2 900
17.6%
1 600
11.8%
/ 600
11.8%
3 600
11.8%
: 600
11.8%
9 300
 
5.9%
300
 
5.9%

freq_16
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing653
Missing (%)68.5%
Memory size7.6 KiB
A
300 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 300
31.5%
(Missing) 653
68.5%

Length

2024-01-10T02:04:08.577940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:08.688200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 300
100.0%

Most occurring characters

ValueCountFrequency (%)
A 300
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 300
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 300
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 300
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 300
100.0%

tra_infr
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing653
Missing (%)68.5%
Memory size7.6 KiB
TOTAL
300 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1500
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 300
31.5%
(Missing) 653
68.5%

Length

2024-01-10T02:04:08.781978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:08.890150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 300
100.0%

Most occurring characters

ValueCountFrequency (%)
T 600
40.0%
O 300
20.0%
A 300
20.0%
L 300
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 600
40.0%
O 300
20.0%
A 300
20.0%
L 300
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 600
40.0%
O 300
20.0%
A 300
20.0%
L 300
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 600
40.0%
O 300
20.0%
A 300
20.0%
L 300
20.0%

n_tracks
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing653
Missing (%)68.5%
Memory size7.6 KiB
TOTAL
300 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1500
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 300
31.5%
(Missing) 653
68.5%

Length

2024-01-10T02:04:08.981771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:09.092109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
total 300
100.0%

Most occurring characters

ValueCountFrequency (%)
T 600
40.0%
O 300
20.0%
A 300
20.0%
L 300
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 600
40.0%
O 300
20.0%
A 300
20.0%
L 300
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 600
40.0%
O 300
20.0%
A 300
20.0%
L 300
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 600
40.0%
O 300
20.0%
A 300
20.0%
L 300
20.0%

ESTAT:TTR00003(1.0)_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing653
Missing (%)68.5%
Memory size7.6 KiB
KM
300 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters600
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKM
2nd rowKM
3rd rowKM
4th rowKM
5th rowKM

Common Values

ValueCountFrequency (%)
KM 300
31.5%
(Missing) 653
68.5%

Length

2024-01-10T02:04:09.181813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:09.294268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
km 300
100.0%

Most occurring characters

ValueCountFrequency (%)
K 300
50.0%
M 300
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 600
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 300
50.0%
M 300
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 300
50.0%
M 300
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 300
50.0%
M 300
50.0%

ESTAT:TTR00003(1.0)_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct202
Distinct (%)67.3%
Missing653
Missing (%)68.5%
Infinite0
Infinite (%)0.0%
Mean8103.4836
Minimum263
Maximum39068.117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:04:09.404972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum263
5-th percentile1163.85
Q12240
median3627
Q310774.75
95-th percentile29617.15
Maximum39068.117
Range38805.117
Interquartile range (IQR)8534.75

Descriptive statistics

Standard deviation9288.367
Coefficient of variation (CV)1.146219
Kurtosis3.1590706
Mean8103.4836
Median Absolute Deviation (MAD)2299
Skewness1.9026826
Sum2431045.1
Variance86273761
MonotonicityNot monotonic
2024-01-10T02:04:09.551803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1209 10
 
1.0%
1839 7
 
0.7%
275 6
 
0.6%
3626 5
 
0.5%
2604 5
 
0.5%
2045 5
 
0.5%
5944 4
 
0.4%
1859.6 4
 
0.4%
1767.6 4
 
0.4%
263 4
 
0.4%
Other values (192) 246
 
25.8%
(Missing) 653
68.5%
ValueCountFrequency (%)
263 4
0.4%
271 2
 
0.2%
275 6
0.6%
1161 3
0.3%
1164 1
 
0.1%
1166 2
 
0.2%
1167 3
0.3%
1175 1
 
0.1%
1196 2
 
0.2%
1208 1
 
0.1%
ValueCountFrequency (%)
39068.117 1
0.1%
38994.798 1
0.1%
38836.096 1
0.1%
38802.643 1
0.1%
38799.773 1
0.1%
38783.143 1
0.1%
38712 1
0.1%
38696.542 1
0.1%
38658.765 1
0.1%
38653.801 1
0.1%

OBS_FLAG_16
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)10.5%
Missing934
Missing (%)98.0%
Memory size7.6 KiB
s
16 
b
3 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rows
2nd rows
3rd rowb
4th rowb
5th rows

Common Values

ValueCountFrequency (%)
s 16
 
1.7%
b 3
 
0.3%
(Missing) 934
98.0%

Length

2024-01-10T02:04:09.693434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:09.817927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
s 16
84.2%
b 3
 
15.8%

Most occurring characters

ValueCountFrequency (%)
s 16
84.2%
b 3
 
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 16
84.2%
b 3
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 19
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 16
84.2%
b 3
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 16
84.2%
b 3
 
15.8%

Interactions

2024-01-10T02:03:24.995970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:51.881915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:54.007625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:56.000845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:57.956330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:59.737223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:01.657673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:03.726672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:05.529495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:07.421202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:09.296823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:11.317211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:13.168788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:14.957185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:16.937762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:19.257209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:21.144636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:23.110003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:25.112326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:52.025484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:54.125329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:56.102142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:58.071228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:59.843390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:01.779720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:03.827815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:05.642963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:07.528937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:09.398754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:11.427519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:13.269229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-01-10T02:03:22.419032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:24.377506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:26.432414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:53.281774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:55.419097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:57.280205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:59.230541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:01.106466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:02.992834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:05.010582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:06.879190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:08.763114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:10.799545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:12.634032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:14.437537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:16.355429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:18.695423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:20.590463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:22.526172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:24.486706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:26.551448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:53.543756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:55.536294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:57.394728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:59.336644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:01.223523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:03.309794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:05.127546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:06.986137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:08.879901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:10.924368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:12.735870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:14.534741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:16.470403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:18.805588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:20.709174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:22.646684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:24.590175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:26.665548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:53.659120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:55.645688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:57.486193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:59.429898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:01.334199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:03.407464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:05.227564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:07.087574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:08.975448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:11.015875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:12.835973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:14.635318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:16.588599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:18.925625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:20.818229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:22.760507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:24.679233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:26.782951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:53.781137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:55.764148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:57.588893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:59.526474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:01.438348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:03.509028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:05.326952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:07.195277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:09.078269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:11.114367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:12.954084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:14.753711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:16.704028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:19.041823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:20.925549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:22.876503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:24.795125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:26.884180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:53.887984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:55.866167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:57.847119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:02:59.621955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:01.554243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:03.609025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:05.427339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:07.297219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:09.182232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:11.215778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:13.054444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:14.855332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:16.804722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:19.141471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:21.035783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:22.978119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:03:24.880023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-01-10T02:04:09.939931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
TIME_PERIODESTAT:ILC_LI02(1.0)_VALUEESTAT:ILC_PW01(1.0)_VALUEESTAT:ILC_PW01(1.0)_VALUE_2ESTAT:ILC_PW01(1.0)_VALUE_3ESTAT:ILC_PW01(1.0)_VALUE_4ESTAT:ILC_PW01(1.0)_VALUE_5ESTAT:ILC_PW01(1.0)_VALUE_6ESTAT:ILC_PW01(1.0)_VALUE_7ESTAT:ILC_PW01(1.0)_VALUE_8ESTAT:ILC_PW01(1.0)_VALUE_9ESTAT:ILC_PW01(1.0)_VALUE_10ESTAT:TOUR_OCC_NINATS(1.0)_VALUEESTAT:RAIL_TF_PASSMOV(1.0)_VALUEESTAT:RAIL_PA_TOTAL(1.0)_VALUEESTAT:RAIL_PA_TOTAL(1.0)_VALUE_14ESTAT:RAIL_AC_CATNMBR(1.0)_VALUEESTAT:TTR00003(1.0)_VALUEgeoOBS_FLAG_11OBS_FLAG_12OBS_FLAG_13OBS_FLAG_14OBS_FLAG_16
TIME_PERIOD1.0000.149NaNNaN0.225NaN0.0870.099NaNNaN0.1060.025-0.029-0.021-0.068-0.062-0.267-0.0030.0640.2160.8770.3740.3740.000
ESTAT:ILC_LI02(1.0)_VALUE0.1491.000-0.721-0.615-0.528-0.544-0.510-0.457-0.501-0.556-0.551-0.487-0.014-0.255-0.236-0.276-0.014-0.0940.5060.6900.8820.6350.6350.000
ESTAT:ILC_PW01(1.0)_VALUENaN-0.7211.0000.7820.8100.7300.7800.8610.7380.8200.7990.757-0.0800.4320.1970.308-0.1350.0281.000NaN0.0001.0001.0001.000
ESTAT:ILC_PW01(1.0)_VALUE_2NaN-0.6150.7821.0000.6640.8460.7470.7580.8690.7190.7250.803-0.1060.217-0.0330.099-0.336-0.2311.000NaN0.0001.0001.0001.000
ESTAT:ILC_PW01(1.0)_VALUE_30.225-0.5280.8100.6641.0000.7850.7660.9440.7640.6680.5570.7890.1040.5730.4250.4900.0750.2260.2201.000NaN1.0001.0001.000
ESTAT:ILC_PW01(1.0)_VALUE_4NaN-0.5440.7300.8460.7851.0000.7670.8460.8920.7640.7430.814-0.1160.3650.1070.191-0.161-0.0241.000NaN0.0001.0001.0001.000
ESTAT:ILC_PW01(1.0)_VALUE_50.087-0.5100.7800.7470.7660.7671.0000.7710.6980.7890.7290.868-0.2610.054-0.0050.059-0.308-0.1650.4841.000NaN1.0001.0001.000
ESTAT:ILC_PW01(1.0)_VALUE_60.099-0.4570.8610.7580.9440.8460.7711.0000.8400.7840.6620.8250.0640.4360.2690.307-0.0590.1270.3691.000NaN1.0001.0000.217
ESTAT:ILC_PW01(1.0)_VALUE_7NaN-0.5010.7380.8690.7640.8920.6980.8401.0000.7210.7180.776-0.0730.3350.0390.155-0.200-0.1221.000NaN0.0001.0001.0001.000
ESTAT:ILC_PW01(1.0)_VALUE_8NaN-0.5560.8200.7190.6680.7640.7890.7840.7211.0000.8240.689-0.2580.284-0.0580.085-0.358-0.2501.000NaN0.0001.0001.0001.000
ESTAT:ILC_PW01(1.0)_VALUE_90.106-0.5510.7990.7250.5570.7430.7290.6620.7180.8241.0000.605-0.1040.1440.1220.183-0.260-0.0990.3861.000NaN1.0001.0000.000
ESTAT:ILC_PW01(1.0)_VALUE_100.025-0.4870.7570.8030.7890.8140.8680.8250.7760.6890.6051.000-0.1720.1320.0600.134-0.271-0.1180.3981.000NaN1.0001.0000.707
ESTAT:TOUR_OCC_NINATS(1.0)_VALUE-0.029-0.014-0.080-0.1060.104-0.116-0.2610.064-0.073-0.258-0.104-0.1721.0000.7780.8330.7750.5580.7100.5650.0001.0001.0001.0000.667
ESTAT:RAIL_TF_PASSMOV(1.0)_VALUE-0.021-0.2550.4320.2170.5730.3650.0540.4360.3350.2840.1440.1320.7781.0000.9810.9620.6070.9010.5190.0000.0000.0000.0000.913
ESTAT:RAIL_PA_TOTAL(1.0)_VALUE-0.068-0.2360.197-0.0330.4250.107-0.0050.2690.039-0.0580.1220.0600.8330.9811.0000.9730.6310.9110.6370.0001.000NaNNaN0.512
ESTAT:RAIL_PA_TOTAL(1.0)_VALUE_14-0.062-0.2760.3080.0990.4900.1910.0590.3070.1550.0850.1830.1340.7750.9620.9731.0000.5700.8400.5800.0001.0001.0001.0000.622
ESTAT:RAIL_AC_CATNMBR(1.0)_VALUE-0.267-0.014-0.135-0.3360.075-0.161-0.308-0.059-0.200-0.358-0.260-0.2710.5580.6070.6310.5701.0000.8370.4161.0001.0001.0001.0001.000
ESTAT:TTR00003(1.0)_VALUE-0.003-0.0940.028-0.2310.226-0.024-0.1650.127-0.122-0.250-0.099-0.1180.7100.9010.9110.8400.8371.0000.8500.3110.8160.9570.9570.738
geo0.0640.5061.0001.0000.2201.0000.4840.3691.0001.0000.3860.3980.5650.5190.6370.5800.4160.8501.0000.8320.8320.9240.9240.874
OBS_FLAG_110.2160.690NaNNaN1.000NaN1.0001.000NaNNaN1.0001.0000.0000.0000.0000.0001.0000.3110.8321.0000.0000.0000.0001.000
OBS_FLAG_120.8770.8820.0000.000NaN0.000NaNNaN0.0000.000NaNNaN1.0000.0001.0001.0001.0000.8160.8320.0001.000NaNNaN0.000
OBS_FLAG_130.3740.6351.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000NaN1.0001.0000.9570.9240.000NaN1.0000.730NaN
OBS_FLAG_140.3740.6351.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000NaN1.0001.0000.9570.9240.000NaN0.7301.000NaN
OBS_FLAG_160.0000.0001.0001.0001.0001.0001.0000.2171.0001.0000.0000.7070.6670.9130.5120.6221.0000.7380.8741.0000.000NaNNaN1.000

Missing values

2024-01-10T02:03:27.435195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T02:03:28.736046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-10T02:03:32.289085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DATAFLOWLAST UPDATEfreqESTAT:ILC_LI02(1.0)_unitindic_ilsexagegeoTIME_PERIODESTAT:ILC_LI02(1.0)_VALUEOBS_FLAGDATAFLOW_1LAST UPDATE_1freq_1ESTAT:ILC_PW01(1.0)_unitisced11indic_wbsex_1age_1ESTAT:ILC_PW01(1.0)_VALUEOBS_FLAG_1DATAFLOW_2LAST UPDATE_2freq_2ESTAT:ILC_PW01(1.0)_unit_2isced11_2indic_wb_2sex_2age_2ESTAT:ILC_PW01(1.0)_VALUE_2OBS_FLAG_2DATAFLOW_3LAST UPDATE_3freq_3ESTAT:ILC_PW01(1.0)_unit_3isced11_3indic_wb_3sex_3age_3ESTAT:ILC_PW01(1.0)_VALUE_3OBS_FLAG_3DATAFLOW_4LAST UPDATE_4freq_4ESTAT:ILC_PW01(1.0)_unit_4isced11_4indic_wb_4sex_4age_4ESTAT:ILC_PW01(1.0)_VALUE_4OBS_FLAG_4DATAFLOW_5LAST UPDATE_5freq_5ESTAT:ILC_PW01(1.0)_unit_5isced11_5indic_wb_5sex_5age_5ESTAT:ILC_PW01(1.0)_VALUE_5OBS_FLAG_5DATAFLOW_6LAST UPDATE_6freq_6ESTAT:ILC_PW01(1.0)_unit_6isced11_6indic_wb_6sex_6age_6ESTAT:ILC_PW01(1.0)_VALUE_6OBS_FLAG_6DATAFLOW_7LAST UPDATE_7freq_7ESTAT:ILC_PW01(1.0)_unit_7isced11_7indic_wb_7sex_7age_7ESTAT:ILC_PW01(1.0)_VALUE_7OBS_FLAG_7DATAFLOW_8LAST UPDATE_8freq_8ESTAT:ILC_PW01(1.0)_unit_8isced11_8indic_wb_8sex_8age_8ESTAT:ILC_PW01(1.0)_VALUE_8OBS_FLAG_8DATAFLOW_9LAST UPDATE_9freq_9ESTAT:ILC_PW01(1.0)_unit_9isced11_9indic_wb_9sex_9age_9ESTAT:ILC_PW01(1.0)_VALUE_9OBS_FLAG_9DATAFLOW_10LAST UPDATE_10freq_10ESTAT:ILC_PW01(1.0)_unit_10isced11_10indic_wb_10sex_10age_10ESTAT:ILC_PW01(1.0)_VALUE_10OBS_FLAG_10DATAFLOW_11LAST UPDATE_11freq_11c_residESTAT:TOUR_OCC_NINATS(1.0)_unithotelsizeESTAT:TOUR_OCC_NINATS(1.0)_VALUEOBS_FLAG_11DATAFLOW_12LAST UPDATE_12freq_12ESTAT:RAIL_TF_PASSMOV(1.0)_unitvehicleESTAT:RAIL_TF_PASSMOV(1.0)_VALUEOBS_FLAG_12DATAFLOW_13LAST UPDATE_13freq_13ESTAT:RAIL_PA_TOTAL(1.0)_unitESTAT:RAIL_PA_TOTAL(1.0)_VALUEOBS_FLAG_13DATAFLOW_14LAST UPDATE_14freq_14ESTAT:RAIL_PA_TOTAL(1.0)_unit_14ESTAT:RAIL_PA_TOTAL(1.0)_VALUE_14OBS_FLAG_14DATAFLOW_15LAST UPDATE_15freq_15ESTAT:RAIL_AC_CATNMBR(1.0)_unitaccidentESTAT:RAIL_AC_CATNMBR(1.0)_VALUEOBS_FLAG_15DATAFLOW_16LAST UPDATE_16freq_16tra_infrn_tracksESTAT:TTR00003(1.0)_unitESTAT:TTR00003(1.0)_VALUEOBS_FLAG_16
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